You can find the list of my scientific publications below. / Vous trouverez ci-dessous la liste de mes publications scientifiques.
Thesis / Thèse :
2018
Bobin, Maxence
Université Paris-Saclay, 2018, (You can find the english abstract on theses.fr in the links section.).
@phdthesis{bobin2018etude,
title = {Étude et conception d’objets de rééducation instrumentés pour le suivi de l’activité motrice des membres supérieurs des patients après un AVC},
author = {Maxence Bobin},
url = {http://theses.fr/2018SACLS419
https://tel.archives-ouvertes.fr/tel-02004344/file/73829_BOBIN_2018_archivage.pdf
http://bobin.iiens.net/wp-content/uploads/2020/03/Soutenance_BOBIN.pptx
},
year = {2018},
date = {2018-11-05},
school = {Université Paris-Saclay},
abstract = {As the population ages, stroke is one of the major health problems affecting 15 million people worldwide each year. After a stroke, many motor and sensory disorders, which have a direct impact on activities of daily living (ADL), can appear as lack of coordination, spasticity or tremors. In order to restore their motor and sensory abilities as much as possible, patients must undergo a long and costly rehabilitation process. In addition, patient monitoring and evaluation is based on visual observations. Little or no objective and quantifiable information on the execution of exercises is collected during rehabilitation sessions. Thus, potentially crucial information on patients' motor abilities is not measured. Many rehabilitation and monitoring platforms have been developed to collect objective information using virtual reality, augmented, interactive screens or portable devices. However, these platforms may have cost, portability and usability constraints.The objective of this thesis is to propose tools to improve the accompaniment of patients during the process of functional rehabilitation by offering health professionals a more representative follow-up of the state of health of these, session after session. This monitoring is carried out using objective and quantifiable information on the motor functions of the upper limbs collected by objects instrumented with sensor. On the basis of the state of the art and the results of an observational study conducted with fourteen health professionals, we developed five instrumented rehabilitation objects for monitoring the motor functions of the hand and arm and the activities of daily life that are materialized by: (i) a jack that collects information on manual dexterity (finger position and pressure, orientation, irregular movements, tremors), (ii) a cube that collects information on overall hand grip (overall finger pressure, orientation, irregular movements and tremors), (iii) a commercially available connected watch that collects information on arm motor functions (arm movements), (iv) an instrumented garment that measures the bending angle of the elbow and (v) a cup that collects information on the motor functions of the arm and hand during complex action sequences (fluid level, orientation, position on a target, finger pressure and tremors) and allows the recognition of activities of daily living such as drinking, sitting, standing, walking, climbing and descending stairs. We conducted an experimental study with fourteen healthcare professionals to explore the functionalities and the design of the objects in order to gather feedback on possible improvements. The results of this study allowed us to tighten the measurement perimeter and make the objects more functional. This study also allowed us to develop visualization interfaces to display data in a simple, fast and easy to interpret manner. We conducted a study with six healthcare professionals to validate the development of these interfaces. The results indicate that the design choices of these visualization interfaces are relevant. Finally, we conducted an experimental usability and acceptability study in nine stroke patients. The results indicate that the efficiency and usability satisfaction criteria are met. The results show that patients found the objects easy to use and are willing to use them during rehabilitation sessions and at home to adapt their rehabilitation program according to their progress.},
note = {You can find the english abstract on theses.fr in the links section.},
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As the population ages, stroke is one of the major health problems affecting 15 million people worldwide each year. After a stroke, many motor and sensory disorders, which have a direct impact on activities of daily living (ADL), can appear as lack of coordination, spasticity or tremors. In order to restore their motor and sensory abilities as much as possible, patients must undergo a long and costly rehabilitation process. In addition, patient monitoring and evaluation is based on visual observations. Little or no objective and quantifiable information on the execution of exercises is collected during rehabilitation sessions. Thus, potentially crucial information on patients' motor abilities is not measured. Many rehabilitation and monitoring platforms have been developed to collect objective information using virtual reality, augmented, interactive screens or portable devices. However, these platforms may have cost, portability and usability constraints.The objective of this thesis is to propose tools to improve the accompaniment of patients during the process of functional rehabilitation by offering health professionals a more representative follow-up of the state of health of these, session after session. This monitoring is carried out using objective and quantifiable information on the motor functions of the upper limbs collected by objects instrumented with sensor. On the basis of the state of the art and the results of an observational study conducted with fourteen health professionals, we developed five instrumented rehabilitation objects for monitoring the motor functions of the hand and arm and the activities of daily life that are materialized by: (i) a jack that collects information on manual dexterity (finger position and pressure, orientation, irregular movements, tremors), (ii) a cube that collects information on overall hand grip (overall finger pressure, orientation, irregular movements and tremors), (iii) a commercially available connected watch that collects information on arm motor functions (arm movements), (iv) an instrumented garment that measures the bending angle of the elbow and (v) a cup that collects information on the motor functions of the arm and hand during complex action sequences (fluid level, orientation, position on a target, finger pressure and tremors) and allows the recognition of activities of daily living such as drinking, sitting, standing, walking, climbing and descending stairs. We conducted an experimental study with fourteen healthcare professionals to explore the functionalities and the design of the objects in order to gather feedback on possible improvements. The results of this study allowed us to tighten the measurement perimeter and make the objects more functional. This study also allowed us to develop visualization interfaces to display data in a simple, fast and easy to interpret manner. We conducted a study with six healthcare professionals to validate the development of these interfaces. The results indicate that the design choices of these visualization interfaces are relevant. Finally, we conducted an experimental usability and acceptability study in nine stroke patients. The results indicate that the efficiency and usability satisfaction criteria are met. The results show that patients found the objects easy to use and are willing to use them during rehabilitation sessions and at home to adapt their rehabilitation program according to their progress.
Journals / Journaux :
2019
Bobin, Maxence; Boukallel, Mehdi; Anastassova, Margarita; Bimbard, Franck; Ammi, Mehdi
SpECTRUM: Smart ECosystem for sTRoke patient's Upper limbs Monitoring Journal Article
In: Smart Health, vol. 13, pp. 100066, 2019, ISSN: 23526483.
@article{bobin2019spectrum,
title = {SpECTRUM: Smart ECosystem for sTRoke patient's Upper limbs Monitoring},
author = {Maxence Bobin and Mehdi Boukallel and Margarita Anastassova and Franck Bimbard and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2020/03/SpECTRUM_Smart_ECosystem_for_sTRoke_patients_Uppe.pdf
https://www.researchgate.net/publication/331007421_SpECTRUM_Smart_ECosystem_for_sTRoke_patient's_Upper_limbs_Monitoring},
doi = {http://dx.doi.org/10.1016/j.smhl.2019.01.001},
issn = {23526483},
year = {2019},
date = {2019-02-07},
journal = {Smart Health},
volume = {13},
pages = {100066},
publisher = {Elsevier},
abstract = {This paper presents a new ecosystem of smart objects designed to monitor motor functions of stroke patients during rehabilitation sessions at the hospital. The ecosystem has been designed starting from an observational study as well as the Action Arm Research Test. It includes a jack and a cube for hand grasping monitoring and a smart watch for the arm dynamic monitoring. The objects embed various sensors able to monitor the pressure of the fingers, the position of the fingers, their orientation, their movements and the tremors of the patient during the manipulation tasks. The developed objects can connect, via Bluetooth or Wi-Fi technology, to an Android mobile application in order to send collected data during the execution of the manipulation task. Performances achieved during the sessions will be displayed on the tablet. Using the collected data, the therapists could assess the upper arm motor abilities of the patient by accessing qualitative information that is usually evaluated by visual estimations or not reported and adapt the rehabilitation program if necessary. The objects, as well as the visualization interfaces, have been evaluated with health care professionals in terms of design and functionalities. The results from this evaluation show that the objects’ design is adapted to bring useful information on the patient’s motor activities, while the visualization interfaces are useful, but require new functionalities. Finally, a preliminary study has been carried out with stroke patients in order to assess the usability and acceptability of such an ecosystem during rehabilitation sessions. This study indicated that the patients are willing to use the ecosystem during the sessions thanks to its easy usage.},
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This paper presents a new ecosystem of smart objects designed to monitor motor functions of stroke patients during rehabilitation sessions at the hospital. The ecosystem has been designed starting from an observational study as well as the Action Arm Research Test. It includes a jack and a cube for hand grasping monitoring and a smart watch for the arm dynamic monitoring. The objects embed various sensors able to monitor the pressure of the fingers, the position of the fingers, their orientation, their movements and the tremors of the patient during the manipulation tasks. The developed objects can connect, via Bluetooth or Wi-Fi technology, to an Android mobile application in order to send collected data during the execution of the manipulation task. Performances achieved during the sessions will be displayed on the tablet. Using the collected data, the therapists could assess the upper arm motor abilities of the patient by accessing qualitative information that is usually evaluated by visual estimations or not reported and adapt the rehabilitation program if necessary. The objects, as well as the visualization interfaces, have been evaluated with health care professionals in terms of design and functionalities. The results from this evaluation show that the objects’ design is adapted to bring useful information on the patient’s motor activities, while the visualization interfaces are useful, but require new functionalities. Finally, a preliminary study has been carried out with stroke patients in order to assess the usability and acceptability of such an ecosystem during rehabilitation sessions. This study indicated that the patients are willing to use the ecosystem during the sessions thanks to its easy usage.
2018
Bobin, Maxence; Anastassova, Margarita; Boukallel, Mehdi; Ammi, Mehdi
Design and study of a smart cup for monitoring the arm and hand activity of stroke patients Journal Article
In: IEEE Journal of Translational Engineering in Health and Medicine, vol. 6, pp. 1–12, 2018, ISSN: 21682372.
@article{bobin2018design,
title = {Design and study of a smart cup for monitoring the arm and hand activity of stroke patients},
author = {Maxence Bobin and Margarita Anastassova and Mehdi Boukallel and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2018/06/Design_and_study_of_a_smart_cup_for_monitoring_the_arm_and_hand_activity_of_stroke_patients-6.pdf
https://www.researchgate.net/publication/327282807_Design_and_study_of_a_smart_cup_for_monitoring_the_arm_and_hand_activity_of_stroke_patients},
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journal = {IEEE Journal of Translational Engineering in Health and Medicine},
volume = {6},
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abstract = {This paper presents a new platform to monitor the arm and hand activity of stroke patients during rehabilitation exercises in hospital and at home during their daily living activities. The platform provides relevant data to the therapist in order to assess the patient’s physical state and adapt the rehabilitation program if necessary. The platform consists of a self-contained smart cup that can be used to perform exercises that are similar to everyday tasks such as drinking. The first smart cup prototype, the design of which was based on interviews regarding the needs of therapists, contains various sensors that collect information about its orientation, the liquid level, its position compared to a reference target and tremors. The prototype also includes audio and visual displays that provide feedback to patients about their movements. Two studies were carried out in conjunction with health care professionals and patients. The first study focused on collecting feedback from health care professionals to assess the functionalities of the cup and to improve the prototype. Based on this study, we designed an improved prototype and created a visualization tool for therapists. Finally, we carried out a preliminary study involving nine patients who had experienced an ischemic or hemorrhagic stroke in the previous 24 months. This preliminary study focused on assessing the usability and acceptability of the cup to the patients. The results showed that the cup was very well accepted by eight of the nine patients in monitoring their activity within a rehabilitation center or at home. Moreover, these eight patients had almost no concerns about the design of the cup and its usability. },
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This paper presents a new platform to monitor the arm and hand activity of stroke patients during rehabilitation exercises in hospital and at home during their daily living activities. The platform provides relevant data to the therapist in order to assess the patient’s physical state and adapt the rehabilitation program if necessary. The platform consists of a self-contained smart cup that can be used to perform exercises that are similar to everyday tasks such as drinking. The first smart cup prototype, the design of which was based on interviews regarding the needs of therapists, contains various sensors that collect information about its orientation, the liquid level, its position compared to a reference target and tremors. The prototype also includes audio and visual displays that provide feedback to patients about their movements. Two studies were carried out in conjunction with health care professionals and patients. The first study focused on collecting feedback from health care professionals to assess the functionalities of the cup and to improve the prototype. Based on this study, we designed an improved prototype and created a visualization tool for therapists. Finally, we carried out a preliminary study involving nine patients who had experienced an ischemic or hemorrhagic stroke in the previous 24 months. This preliminary study focused on assessing the usability and acceptability of the cup to the patients. The results showed that the cup was very well accepted by eight of the nine patients in monitoring their activity within a rehabilitation center or at home. Moreover, these eight patients had almost no concerns about the design of the cup and its usability.
International conferences / Conférences internationales :
2018
Bobin, Maxence; Anastassova, Margarita; Boukallel, Mehdi; Bimbard, Franck; Ammi, Mehdi
Smart Objects Ecosystem for Post-Stroke Upper Limbs' Motor Functions Monitoring Conference
Proceedings of the 1st International Conference on Digital Tools & Uses Congress, Association for Computing Machinery, New York, NY, United States, 2018, ISBN: 978-1-4503-6451-5.
@conference{bobin2018smart,
title = {Smart Objects Ecosystem for Post-Stroke Upper Limbs' Motor Functions Monitoring},
author = {Maxence Bobin and Margarita Anastassova and Mehdi Boukallel and Franck Bimbard and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2018/08/smart-objects-ecosystem-6.pdf
https://www.researchgate.net/publication/327284896_Smart_Objects_Ecosystem_for_Post-Stroke_Upper_Limbs'_Motor_Functions_Monitoring},
doi = {https://doi.org/10.1145/3240117.3240133},
isbn = {978-1-4503-6451-5},
year = {2018},
date = {2018-10-10},
booktitle = {Proceedings of the 1st International Conference on Digital Tools & Uses Congress},
pages = {1--4},
publisher = {Association for Computing Machinery},
address = {New York, NY, United States},
abstract = {This paper presents a new ecosystem of smart objects inspired by the Action Arm Research Test used to assess the stroke patient's motor functions. This ecosystem aims to collect objective and quantifiable information on upper limbs motor functions of stroke patients by using physical objects manipulated by the patients without overloading cognitive functions with virtual environments or without asking the patients to wear sensors. The ecosystem is composed of a jack and a cube embedding sensors designed to monitor the hand prehension (fingers movements, fingers precision, global hand movements) and a wrist band devoted to monitor the arm movements during rehabilitation exercises. The therapists can easily collect, visualize and analyze the patient's data through a mobile application developed to present a summary of the patient's state at the end of the session and allowing to compare records of different sessions to assess the patient's evolution. },
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This paper presents a new ecosystem of smart objects inspired by the Action Arm Research Test used to assess the stroke patient's motor functions. This ecosystem aims to collect objective and quantifiable information on upper limbs motor functions of stroke patients by using physical objects manipulated by the patients without overloading cognitive functions with virtual environments or without asking the patients to wear sensors. The ecosystem is composed of a jack and a cube embedding sensors designed to monitor the hand prehension (fingers movements, fingers precision, global hand movements) and a wrist band devoted to monitor the arm movements during rehabilitation exercises. The therapists can easily collect, visualize and analyze the patient's data through a mobile application developed to present a summary of the patient's state at the end of the session and allowing to compare records of different sessions to assess the patient's evolution.
Bobin, Maxence; Amroun, Hamdi; Boukallel, Mehdi; Anastassova, Margarita; Ammi, Mehdi
Smart Cup to Monitor Stroke Patients Activities during Everyday Life Conference
2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), IEEE 2018, ISBN: 9781538679753.
@conference{bobin2018smart,
title = {Smart Cup to Monitor Stroke Patients Activities during Everyday Life},
author = {Maxence Bobin and Hamdi Amroun and Mehdi Boukallel and Margarita Anastassova and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2018/08/smart-cup-monitor-6.pdf
http://bobin.iiens.net/wp-content/uploads/2018/08/ithings.pptx
https://www.researchgate.net/publication/327284275_Smart_Cup_to_Monitor_Stroke_Patients_Activities_during_Everyday_Life},
doi = {http://dx.doi.org/10.1109/Cybermatics_2018.2018.00062},
isbn = {9781538679753},
year = {2018},
date = {2018-08-02},
booktitle = {2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)},
journal = {iThings, 2018 IEEE 11th International Conference on Internet of Things, 2018},
pages = {189--195},
organization = {IEEE},
abstract = {This paper presents a new platform to monitor stroke patients activities during their everyday life at home. This platform is intended to be a part of a smart objects ecosystem for home monitoring using common objects embedding sensors. The monitoring is performed with a self-contained smart cup that can be used to drink at different times of the day. The smart cup embeds various sensors in order to detect its movements and the liquid level. Activity analysis is performed on the collected data in order to provide information to the therapists on the patient's sedentariness and independence on the daily life tasks (sitting, walking, drinking and going up and down the stairs). This paper presents the design concept of the smart cup along with the implementation and mainly focuses on the activity analysis process. We used a linear classifier: the Support Vector Machine (SVM) classifier. Indeed, the classification of stroke patient's activities is a binary classification. Moreover, as we decided to use DCT features, SVM is the classifier that gives better classification performances. The results show a recognition precision above 92% on all activities with the smart cup. A comparative study has been carried out in order to assess the performances of the linear SVM classifier and a non-linear Multi-Layered Perceptron (MLP) classifier. The result of this study shows that the linear SVM classifier offers better performances on classifying everyday life activities with a smart cup. },
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This paper presents a new platform to monitor stroke patients activities during their everyday life at home. This platform is intended to be a part of a smart objects ecosystem for home monitoring using common objects embedding sensors. The monitoring is performed with a self-contained smart cup that can be used to drink at different times of the day. The smart cup embeds various sensors in order to detect its movements and the liquid level. Activity analysis is performed on the collected data in order to provide information to the therapists on the patient's sedentariness and independence on the daily life tasks (sitting, walking, drinking and going up and down the stairs). This paper presents the design concept of the smart cup along with the implementation and mainly focuses on the activity analysis process. We used a linear classifier: the Support Vector Machine (SVM) classifier. Indeed, the classification of stroke patient's activities is a binary classification. Moreover, as we decided to use DCT features, SVM is the classifier that gives better classification performances. The results show a recognition precision above 92% on all activities with the smart cup. A comparative study has been carried out in order to assess the performances of the linear SVM classifier and a non-linear Multi-Layered Perceptron (MLP) classifier. The result of this study shows that the linear SVM classifier offers better performances on classifying everyday life activities with a smart cup.
Bobin, Maxence; Amroun, Hamdi; Anastassova, Margarita; Boukallel, Mehdi; Ammi, Mehdi
Smart Cup for Festival Alcohol Consumption Awareness Conference
2018 IEEE 4th World Forum on Internet of Things (WF-IoT), IEEE 2018, ISBN: 9781467399449.
@conference{bobin2018smart,
title = {Smart Cup for Festival Alcohol Consumption Awareness},
author = {Maxence Bobin and Hamdi Amroun and Margarita Anastassova and Mehdi Boukallel and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2018/03/SmartCupforFestivalAlcoholConsumption.pdf
http://bobin.iiens.net/wp-content/uploads/2018/03/Smart-Cup-for-Festival-Consumption-Awareness.pptx
https://www.researchgate.net/publication/323416094_Smart_Cup_for_Festival_Alcohol_Consumption_Awareness},
doi = {http://dx.doi.org/10.1109/WF-IoT.2018.8355099},
isbn = {9781467399449},
year = {2018},
date = {2018-02-08},
booktitle = {2018 IEEE 4th World Forum on Internet of Things (WF-IoT)},
journal = {Internet of Things (WF-IoT), 2018 IEEE 4th World Forum},
pages = {718-723},
organization = {IEEE},
abstract = {This paper presents a platform prototype to monitor alcohol consumption for festival's attendees. The platform also aims to raise awareness about alcohol consumption in order to help people control or even reduce their alcohol consumption. The platform consists of a self-contained smart cup which the attendee uses during the day. This platform allows to easily follow the user's alcohol intake based on a model in order to display alerts if necessary. The smart cup embeds sensors to detect the liquid level and its movements. In addition, activity recognition is performed from the collected data in order to recognize when the user drinks and enhance the alcohol intake assessment. The smart cup also embeds LEDs visual displays that provides information about the alcohol intake estimation. This paper presents the design concept of the smart cup along with the technical implementation. Then, the data processing and analysis is presented for the alcohol intake estimation method using activity recognition with a Support Vector Machine classifier. An experiment have also been carried out and shows a good level of recognition above 90% for the "drinking" activity. Next, the feedback identification for the alcohol intake estimation is detailed in the last section. Finally, perspectives for the detection of abnormal behavior based on the alcohol intake assessment and the movements of the cup are introduced.
},
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This paper presents a platform prototype to monitor alcohol consumption for festival's attendees. The platform also aims to raise awareness about alcohol consumption in order to help people control or even reduce their alcohol consumption. The platform consists of a self-contained smart cup which the attendee uses during the day. This platform allows to easily follow the user's alcohol intake based on a model in order to display alerts if necessary. The smart cup embeds sensors to detect the liquid level and its movements. In addition, activity recognition is performed from the collected data in order to recognize when the user drinks and enhance the alcohol intake assessment. The smart cup also embeds LEDs visual displays that provides information about the alcohol intake estimation. This paper presents the design concept of the smart cup along with the technical implementation. Then, the data processing and analysis is presented for the alcohol intake estimation method using activity recognition with a Support Vector Machine classifier. An experiment have also been carried out and shows a good level of recognition above 90% for the "drinking" activity. Next, the feedback identification for the alcohol intake estimation is detailed in the last section. Finally, perspectives for the detection of abnormal behavior based on the alcohol intake assessment and the movements of the cup are introduced.
- http://bobin.iiens.net/wp-content/uploads/2018/03/SmartCupforFestivalAlcoholCons[...]
- http://bobin.iiens.net/wp-content/uploads/2018/03/Smart-Cup-for-Festival-Consump[...]
- https://www.researchgate.net/publication/323416094_Smart_Cup_for_Festival_Alcoho[...]
- doi:http://dx.doi.org/10.1109/WF-IoT.2018.8355099
2017
Bobin, Maxence; Amroun, Hamdi; Coquillart, Sabine; Ammi, Mehdi
DNN based Approach for the Assessment of Elbow Flexion with Smart Textile Sensor Conference
Systems, Man, and Cybernetics, IEEE 2017, ISBN: 9781538616451, (WARNING : Error in title. IEEE title is "SVM based approach for the assessment of elbow flexion with smart textile sensor" while DNN is used in this paper.).
@conference{Bobin2017d,
title = {DNN based Approach for the Assessment of Elbow Flexion with Smart Textile Sensor},
author = {Maxence Bobin and Hamdi Amroun and Sabine Coquillart and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2018/03/SMCTextile17-main.pdf
https://www.researchgate.net/publication/320183453_DNN_based_Approach_for_the_Assessment_of_Elbow_Flexion_with_Smart_Textile_Sensor},
doi = {http://dx.doi.org/10.1109/SMC.2017.8122934},
isbn = {9781538616451},
year = {2017},
date = {2017-10-10},
booktitle = {Systems, Man, and Cybernetics},
journal = {Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference},
pages = {2129-2134},
organization = {IEEE},
abstract = {This paper presents a new approach for monitoring the elbow flexion with a smart textile based on the piezo-resistive effect. The sensor is composed of a mix of conductive threads made of stainless steel fibers doped with metal particles and dielectric silk threads. The threads are arranged in a double bridle crochet structure to provide the best compromise between mechanical elasticity and sensor sensitivity. The sensor was integrated to a sweater to measure the joint angle of the elbow. In order to asses the angle in robust and accurate way, we developed a static model for angle recognition based on a machine learning algorithm: a Deep Neural Network. The design process of the static model includes the data collection, the extraction of features and the choice of the best architecture for the Deep Neural Network. This approach allow to reach recognition accuracy between 76.9% and 83.66%. Finally, a comparative study have also been performed in order to compare the performances of our model with different algorithms such as SVM, NB and DT. },
note = {WARNING : Error in title. IEEE title is "SVM based approach for the assessment of elbow flexion with smart textile sensor" while DNN is used in this paper.},
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This paper presents a new approach for monitoring the elbow flexion with a smart textile based on the piezo-resistive effect. The sensor is composed of a mix of conductive threads made of stainless steel fibers doped with metal particles and dielectric silk threads. The threads are arranged in a double bridle crochet structure to provide the best compromise between mechanical elasticity and sensor sensitivity. The sensor was integrated to a sweater to measure the joint angle of the elbow. In order to asses the angle in robust and accurate way, we developed a static model for angle recognition based on a machine learning algorithm: a Deep Neural Network. The design process of the static model includes the data collection, the extraction of features and the choice of the best architecture for the Deep Neural Network. This approach allow to reach recognition accuracy between 76.9% and 83.66%. Finally, a comparative study have also been performed in order to compare the performances of our model with different algorithms such as SVM, NB and DT.
2016
Bobin, Maxence; Anastassova, Margarita; Boukallel, Mehdi; Ammi, Mehdi
SyMPATHy: Smart glass for Monitoring and guiding stroke PATients in a Home-based context Conference
Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, 2016, ISBN: 9781450343220.
@conference{bobin2016sympathy,
title = {SyMPATHy: Smart glass for Monitoring and guiding stroke PATients in a Home-based context},
author = {Maxence Bobin and Margarita Anastassova and Mehdi Boukallel and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2018/02/SyMPATHySmartglassforMonitoringandguidingstrokePATientsinaHome-basedcontext.pdf
https://www.researchgate.net/publication/303972319_SyMPATHy_Smart_glass_for_Monitoring_and_guiding_stroke_PATients_in_a_Home-based_context},
doi = {http://dx.doi.org/10.1145/2933242.2935870},
isbn = {9781450343220},
year = {2016},
date = {2016-06-23},
booktitle = {Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems},
journal = {Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems},
pages = {281--286},
abstract = {This paper presents a solution to monitor and guide stroke patients during Activities of the Daily Living. It consists of a self-content smart glass that the patient can use to drink at different times of the day (water, coffee, etc.). The smart glass embeds a series of sensors that track in a transparent way the patients activity in everyday life (glass orientation, liquid level, target reaching and tremors). This solution allows therapists to monitor and analyze easily the Activities of the Daily Living of the patient in order to adapt the weekly rehabilitation sessions with suitable exercises. In addition, the smart glass embeds visual displays aimed at providing gestural guidance information when the patient do not use properly the glass. The paper presents the first prototype of the smart glass by highlighting the methodology adopted to design the software and hardware components of the platform. },
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This paper presents a solution to monitor and guide stroke patients during Activities of the Daily Living. It consists of a self-content smart glass that the patient can use to drink at different times of the day (water, coffee, etc.). The smart glass embeds a series of sensors that track in a transparent way the patients activity in everyday life (glass orientation, liquid level, target reaching and tremors). This solution allows therapists to monitor and analyze easily the Activities of the Daily Living of the patient in order to adapt the weekly rehabilitation sessions with suitable exercises. In addition, the smart glass embeds visual displays aimed at providing gestural guidance information when the patient do not use properly the glass. The paper presents the first prototype of the smart glass by highlighting the methodology adopted to design the software and hardware components of the platform.
Book chapters / Chapitres d’ouvrage :
2018
Study,; of a Smart Cup for Monitoring Post-stroke Patients' Activities at Home, Development
Study and Development of a Smart Cup for Monitoring Post‐stroke Patients' Activities at Home Book Chapter
In: Bobin, Maxence; Boukallel, Mehdi; Anastassova, Margarita; Ammi, Mehdi (Ed.): Challenges of the Internet of Things: Technology, Use, Ethics, vol. 7, pp. 53–76, Wiley Online Library, 2018, ISBN: 9781786303615.
@inbook{ammi2018study,
title = {Study and Development of a Smart Cup for Monitoring Post‐stroke Patients' Activities at Home},
author = {Study and Development of a Smart Cup for Monitoring Post-stroke Patients' Activities at Home},
editor = {Maxence Bobin and Mehdi Boukallel and Margarita Anastassova and Mehdi Ammi},
doi = {https://doi.org/10.1002/9781119549765.ch3},
isbn = {9781786303615},
year = {2018},
date = {2018-09-28},
booktitle = {Challenges of the Internet of Things: Technology, Use, Ethics},
volume = {7},
pages = {53--76},
publisher = {Wiley Online Library},
abstract = {This chapter presents the state of the art for motor assessment of the upper limbs, activity analysis and post‐stroke rehabilitation. It also presents the design methodology and the prototype of SyMPATHy, a self‐contained smart cup for monitoring post‐stroke patients at home. SyMPATHy is the fundamental element of an ecosystem of smart objects for home monitoring. The design process of SyMPATHy includes three main steps: identification, implementation of the prototype, and data processing for orientation detection, tremor detection and characterization, and activity recognition. On the basis of the prototype of SyMPATHy, two studies are planned with therapists and patients. The first objective is to collect feedback in order to improve the design and functionalities of the cup. The usability and acceptability of the platform are also studied. Finally, the impact of SyMPATHy on the health status of patients is assessed.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
This chapter presents the state of the art for motor assessment of the upper limbs, activity analysis and post‐stroke rehabilitation. It also presents the design methodology and the prototype of SyMPATHy, a self‐contained smart cup for monitoring post‐stroke patients at home. SyMPATHy is the fundamental element of an ecosystem of smart objects for home monitoring. The design process of SyMPATHy includes three main steps: identification, implementation of the prototype, and data processing for orientation detection, tremor detection and characterization, and activity recognition. On the basis of the prototype of SyMPATHy, two studies are planned with therapists and patients. The first objective is to collect feedback in order to improve the design and functionalities of the cup. The usability and acceptability of the platform are also studied. Finally, the impact of SyMPATHy on the health status of patients is assessed.
2017
Bobin, Maxence; Boukallel, Mehdi; Anastassova, Margarita; Ammi, Mehdi
Étude et développement d'un verre intelligent pour le suivi d'activité à domicile des patients post-AVC Book Chapter
In: vol. 17, ISTE OpenScience Internet des objets, 2017, ISSN: 25148273.
@inbook{Bobin2017b,
title = {Étude et développement d'un verre intelligent pour le suivi d'activité à domicile des patients post-AVC},
author = {Maxence Bobin and Mehdi Boukallel and Margarita Anastassova and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2018/08/Etude_et_developpement_dun_verre_intelligent_pour.pdf
https://www.researchgate.net/publication/316469179_Etude_et_developpement_d'un_verre_intelligent_pour_le_suivi_d'activite_a_domicile_des_patients_post-AVC},
doi = {http://dx.doi.org/10.21494/ISTE.OP.2017.0135},
issn = {25148273},
year = {2017},
date = {2017-04-03},
volume = {17},
publisher = {ISTE OpenScience Internet des objets},
abstract = {In this paper, we present a platform for continuously monitoring and guiding post-stroke patients at home during their daily activities. The platform consists of a smart cup that carries sensors to monitor the arm and hand activity during the day. The cup detects its orientation, the water level, its position relative to a specific target as well as the hand tremors. In addition, display can guide the patient in his movements. We also present two planned clinical studies with therapists and patients.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
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In this paper, we present a platform for continuously monitoring and guiding post-stroke patients at home during their daily activities. The platform consists of a smart cup that carries sensors to monitor the arm and hand activity during the day. The cup detects its orientation, the water level, its position relative to a specific target as well as the hand tremors. In addition, display can guide the patient in his movements. We also present two planned clinical studies with therapists and patients.
Posters / Posters :
2017
Bobin, Maxence; Boukallel, Mehdi; Anastassova, Margarita; Ammi, Mehdi
Smart objects for upper limb monitoring of stroke patients during rehabilitation sessions Presentation
09.11.2017.
@misc{Bobin2017,
title = {Smart objects for upper limb monitoring of stroke patients during rehabilitation sessions},
author = {Maxence Bobin and Mehdi Boukallel and Margarita Anastassova and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2018/08/posterBOBIN_FEDEV.pdf
https://www.researchgate.net/publication/327285107_Smart_objects_for_upper_limb_monitoring_of_stroke_patients_during_rehabilitation_sessions},
year = {2017},
date = {2017-11-09},
publisher = {Journée FéDeV},
abstract = {Les Accidents Vasculaires Cérébraux (AVC) touchent de plus en plus de personnes dans le monde et peuvent entrainer des déficits cognitifs et moteurs importants (paralysie spasmodique, faiblesse musculaire, troubles oculaires etc). 15 millions de personnes en sont victimes chaque année dont 5 millions décèdent et 5 millions d’entre elles demeurent handicapées. Bien que différentes méthodes aient émergé pour évaluer l'état de récupération des patients, en particulier la récupération des membres supérieurs, les mesures restent empiriques et sont basées sur des estimations visuelles. Les séances de rééducation des membres supérieurs en centre sont effectuées à l’aide d’outils non instrumentés et parfois même fait maison. Aucun suivi quantifié n’est effectué pendant les séances de rééducation. Seuls les bilans permettent de quantifier empiriquement les progrès des patients et d’évaluer la préhension du patient à l’aide d’outils de mesure de force (poire de pression, manomètre etc.). De plus, les patients sont majoritairement seuls pendant ces exercices et les ergothérapeutes naviguent entre leurs patients. Cependant, l’émergence de l’Internet des Objets permet de proposer des solutions flexibles pour le suivi de l’activité motrice du membre supérieur en centre de rééducation. Une étude observationnelle a été menée auprès de 10 ergothérapeutes durant des séances de rééducation des membres supérieurs afin de découvrir sur le terrain les objets utilisés avec les patients. Nous avons assisté aux séances de rééducation et nous avons pu accéder à tous les outils de rééducation du centre. Nous avons pu également nous entretenir avec les ergothérapeutes afin de recueillir leurs attentes. Ces entretiens ont permis de rédiger une première liste de plusieurs recommandations pour la conception de divers objets instrumentés pour le suivi d’activité motrice des membres supérieurs des patients post-AVC. Les principaux objets utilisés durant les séances sont des objets destinés à la saisie (à deux ou trois doigts ainsi qu’avec la main). Le paramètre qui intéresse le plus les ergothérapeutes est la pression exercée par le patient sur l’objet. D’autre part, le bilan de motricité des membres supérieurs est effectué à l’aide de protocoles tels que ARAT qui se révèle complet et efficace. Enfin, après les séances, les patients effectuent un dernier exercice : le pendule. Appuyé le coude sur une table, ils doivent laisser se balancer le bras opposé naturellement afin de décoapter et de relâcher l’épaule. Notre contribution s’insère dans une démarche d’amélioration du suivi des patients en apportant des données quantifiables et pertinentes aux ergothérapeutes sans bouleverser leur travail et la vie des patients. Nous avons décidé d’instrumenter les objets utilisés dans le protocole ARAT ou similaire afin de collecter des données et d’améliorer le bilan moteur du patient. Les objets qui seront instrumentés seront les suivants : (1) un osselet pour suivre la préhension fine des doigts, (2) un cube utilisé dans le protocole ARAT et (3) un bracelet permettant de remonter à l’activité motrice du bras. L’osselet est composé de quatre capteurs de pression et de position placés sur sa tranche afin de remonter à la configuration de la main lors de la saisie ainsi qu’aux forces appliquées sur chaque point de contact. Il embarque également une centrale inertielle afin de détecter des tremblements et des mouvements anormaux. Le cube embarque également des capteurs de pression sur chacune de ses faces ainsi qu’une centrale inertielle. Enfin, le bracelet se compose uniquement d’une centrale inertielle afin de détecter si les mouvements de pendule sont volontaires ou non. La transmission des données s’effectue en temps réel via Bluetooth Low Energy (BLE) et une application de visualisation des données permet au médecins de suivre l’évolution de la pression sur l’objet en temps réel. Une étude est en prévision afin d’évaluer ces objets en conditions réelles avec des patients en centre de rééducation.},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Les Accidents Vasculaires Cérébraux (AVC) touchent de plus en plus de personnes dans le monde et peuvent entrainer des déficits cognitifs et moteurs importants (paralysie spasmodique, faiblesse musculaire, troubles oculaires etc). 15 millions de personnes en sont victimes chaque année dont 5 millions décèdent et 5 millions d’entre elles demeurent handicapées. Bien que différentes méthodes aient émergé pour évaluer l'état de récupération des patients, en particulier la récupération des membres supérieurs, les mesures restent empiriques et sont basées sur des estimations visuelles. Les séances de rééducation des membres supérieurs en centre sont effectuées à l’aide d’outils non instrumentés et parfois même fait maison. Aucun suivi quantifié n’est effectué pendant les séances de rééducation. Seuls les bilans permettent de quantifier empiriquement les progrès des patients et d’évaluer la préhension du patient à l’aide d’outils de mesure de force (poire de pression, manomètre etc.). De plus, les patients sont majoritairement seuls pendant ces exercices et les ergothérapeutes naviguent entre leurs patients. Cependant, l’émergence de l’Internet des Objets permet de proposer des solutions flexibles pour le suivi de l’activité motrice du membre supérieur en centre de rééducation. Une étude observationnelle a été menée auprès de 10 ergothérapeutes durant des séances de rééducation des membres supérieurs afin de découvrir sur le terrain les objets utilisés avec les patients. Nous avons assisté aux séances de rééducation et nous avons pu accéder à tous les outils de rééducation du centre. Nous avons pu également nous entretenir avec les ergothérapeutes afin de recueillir leurs attentes. Ces entretiens ont permis de rédiger une première liste de plusieurs recommandations pour la conception de divers objets instrumentés pour le suivi d’activité motrice des membres supérieurs des patients post-AVC. Les principaux objets utilisés durant les séances sont des objets destinés à la saisie (à deux ou trois doigts ainsi qu’avec la main). Le paramètre qui intéresse le plus les ergothérapeutes est la pression exercée par le patient sur l’objet. D’autre part, le bilan de motricité des membres supérieurs est effectué à l’aide de protocoles tels que ARAT qui se révèle complet et efficace. Enfin, après les séances, les patients effectuent un dernier exercice : le pendule. Appuyé le coude sur une table, ils doivent laisser se balancer le bras opposé naturellement afin de décoapter et de relâcher l’épaule. Notre contribution s’insère dans une démarche d’amélioration du suivi des patients en apportant des données quantifiables et pertinentes aux ergothérapeutes sans bouleverser leur travail et la vie des patients. Nous avons décidé d’instrumenter les objets utilisés dans le protocole ARAT ou similaire afin de collecter des données et d’améliorer le bilan moteur du patient. Les objets qui seront instrumentés seront les suivants : (1) un osselet pour suivre la préhension fine des doigts, (2) un cube utilisé dans le protocole ARAT et (3) un bracelet permettant de remonter à l’activité motrice du bras. L’osselet est composé de quatre capteurs de pression et de position placés sur sa tranche afin de remonter à la configuration de la main lors de la saisie ainsi qu’aux forces appliquées sur chaque point de contact. Il embarque également une centrale inertielle afin de détecter des tremblements et des mouvements anormaux. Le cube embarque également des capteurs de pression sur chacune de ses faces ainsi qu’une centrale inertielle. Enfin, le bracelet se compose uniquement d’une centrale inertielle afin de détecter si les mouvements de pendule sont volontaires ou non. La transmission des données s’effectue en temps réel via Bluetooth Low Energy (BLE) et une application de visualisation des données permet au médecins de suivre l’évolution de la pression sur l’objet en temps réel. Une étude est en prévision afin d’évaluer ces objets en conditions réelles avec des patients en centre de rééducation.
2016
Bobin, Maxence; Boukallel, Mehdi; Anastassova, Margarita; Ammi, Mehdi
Étude d'un verre intelligent pour le suivi d'activité à domicile des patients post-AVC. Presentation
29.11.2016.
@misc{Bobin2016,
title = {Étude d'un verre intelligent pour le suivi d'activité à domicile des patients post-AVC.},
author = {Maxence Bobin and Mehdi Boukallel and Margarita Anastassova and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2018/08/sig_alternate_sample-ilovepdf-compressed.pdf
https://www.researchgate.net/publication/327285224_Etude_d%27un_verre_intelligent_pour_le_suivi_d%27activite_a_domicile_des_patients_post-AVC},
year = {2016},
date = {2016-11-29},
publisher = {Journée Nationale de l'Internet des Objets},
abstract = {Dans ce papier, nous présentons une plateforme de suivi d’activité continu et de guidage des patients post-AVC à domicile durant leurs activités de la vie quotidienne. La plateforme consiste en un verre intelligent qui embarque des capteurs afin de suivre l’activité du bras et de la main lors de la journée. Le verre détecte son orientation, le niveau d’eau, sa position par rapport à une cible spécifique ainsi que les tremblements. De plus, un affichage permet de guider le patient dans ses mouvements. Nous présentons également deux études prévues avec les thérapeutes et les patients.},
type = {poster},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Dans ce papier, nous présentons une plateforme de suivi d’activité continu et de guidage des patients post-AVC à domicile durant leurs activités de la vie quotidienne. La plateforme consiste en un verre intelligent qui embarque des capteurs afin de suivre l’activité du bras et de la main lors de la journée. Le verre détecte son orientation, le niveau d’eau, sa position par rapport à une cible spécifique ainsi que les tremblements. De plus, un affichage permet de guider le patient dans ses mouvements. Nous présentons également deux études prévues avec les thérapeutes et les patients.
Bobin, Maxence; Boukallel, Mehdi; Anastassova, Margarita; Ammi, Mehdi
Study of a Smart Cup for Home Monitoring of the Arm and Hand of Stroke Patients Presentation
10.10.2016.
@misc{Bobin2016b,
title = {Study of a Smart Cup for Home Monitoring of the Arm and Hand of Stroke Patients},
author = {Maxence Bobin and Mehdi Boukallel and Margarita Anastassova and Mehdi Ammi},
url = {http://bobin.iiens.net/wp-content/uploads/2018/03/StudyofaSmartCupforHomeMonitoringoftheArmandHandofStrokePatients-ilovepdf-compressed-1.pdf
http://bobin.iiens.net/wp-content/uploads/2018/03/poster_Assets_BOBIN2.pdf
https://www.researchgate.net/publication/309328281_Study_of_a_Smart_Cup_for_Home_Monitoring_of_the_Arm_and_Hand_of_Stroke_Patients},
doi = {10.1145/2982142.2982188},
year = {2016},
date = {2016-10-10},
journal = {Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility},
number = {305-306},
abstract = {In this work, we present a platform for continuously monitor and guide stroke patient at home during Activities of the Daily Living. The platform consists of a smart cup which embeds sensors that monitor the patient's hand and arm motor activity at different times of the day. The cup detects its orientation, the liquid level, its position on a specific target, as well as tremors. Moreover, displays are provided to guide the patient's movement. Finally, the planned studies with both the therapists and patients are presented. },
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
In this work, we present a platform for continuously monitor and guide stroke patient at home during Activities of the Daily Living. The platform consists of a smart cup which embeds sensors that monitor the patient's hand and arm motor activity at different times of the day. The cup detects its orientation, the liquid level, its position on a specific target, as well as tremors. Moreover, displays are provided to guide the patient's movement. Finally, the planned studies with both the therapists and patients are presented.
Patents / Brevets :
2017
Bobin, Maxence; Bimbard, Franck; Coquillart, Sabine; Ammi, Mehdi
Procédé de conception d’un capteur de flexion textile piézorésistif à partir de fils fonctionnels. Patent
2017.
@patent{Bobin2017c,
title = {Procédé de conception d’un capteur de flexion textile piézorésistif à partir de fils fonctionnels.},
author = {Maxence Bobin and Franck Bimbard and Sabine Coquillart and Mehdi Ammi},
year = {2017},
date = {2017-04-05},
publisher = {INPI},
keywords = {},
pubstate = {published},
tppubtype = {patent}
}