Contactless Multi-Sensor Solution for E-Treatment of Musculoskeletal Disorders

The treatment of Musculoskeletal Disorders requires physical exercises to be executed regularly and correctly. This paper presents a technological solution to support this therapy consisting of on-body sensors and a supporting platform that captures and forwards the data automatically. We implemented wireless communication and charging in the sensor module. The wireless functionality enhances patient’s comfort during movements and allows for easy and hygienic maintenance. Validation in a real-life case has demonstrated the suitability of the solution to monitor treatment remotely. Furthermore, it was shown to be easy-to-use by non-technical experts. The sensor has an autonomy of 20 hours, which contributes to its user friendliness. We explain and share the designs that have been realized with low cost IoT technologies including Micro-Electro-Mechanical System sensors, low power wireless connectivity and microcontrollers. The contactless multi-sensor solution can be broadly adopted both in medical cabinets and for remote treatment. The latter is of specific interest when the patient is far from the therapist, encounters mobility problems, or when safe distancing is needed.

[1]  B. Freriks,et al.  Development of recommendations for SEMG sensors and sensor placement procedures. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[2]  Linda Foyle,et al.  Musculoskeletal conditions. , 2008, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[3]  Haseeb Ur Rahman,et al.  An Energy-Efficient and Cooperative Fault- Tolerant Communication Approach for Wireless Body Area Network , 2020, IEEE Access.

[4]  Evangelos Dermatas,et al.  On the development of a wireless motion capture sensor node for upper limb rehabilitation , 2019, 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT).

[5]  Uriel Martinez-Hernandez,et al.  Simultaneous Bayesian Recognition of Locomotion and Gait Phases With Wearable Sensors , 2018, IEEE Sensors Journal.

[6]  Angelica Munoz-Melendez,et al.  Wearable Inertial Sensors for Human Motion Analysis: A Review , 2016, IEEE Sensors Journal.

[7]  Hong-Ren Chen,et al.  Design of Motion Sensing Martial Art Learning System , 2019, 2019 International Conference on Intelligent Computing and its Emerging Applications (ICEA).

[8]  Huosheng Hu,et al.  Human motion tracking for rehabilitation - A survey , 2008, Biomed. Signal Process. Control..

[9]  Christopher G. Pretty,et al.  Low-cost active electromyography , 2019 .

[10]  Niclas Roxhed,et al.  Integrating MEMS and ICs , 2015 .

[11]  Brendan O'Flynn,et al.  A Multi-Sensors Wearable System for Remote Assessment of Physiotherapy Exercises during ACL Rehabilitation , 2019, 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS).

[12]  A. Cotten,et al.  Musculoskeletal Disorders in the Elderly , 2012, Journal of clinical imaging science.

[13]  Hadi Heidari,et al.  Multisensor data fusion for human activities classification and fall detection , 2017, 2017 IEEE SENSORS.

[14]  J. Fraden,et al.  Handbook of Modern Sensors: Physics, Designs, and Applications, 2nd ed. , 1998 .