Design of a Wearable Sensing System for Human Motion Monitoring in Physical Rehabilitation

Human motion monitoring and analysis can be an essential part of a wide spectrum of applications, including physical rehabilitation among other potential areas of interest. Creating non-invasive systems for monitoring patients while performing rehabilitation exercises, to provide them with an objective feedback, is one of the current challenges. In this paper we present a wearable multi-sensor system for human motion monitoring, which has been developed for use in rehabilitation. It is composed of a number of small modules that embed high-precision accelerometers and wireless communications to transmit the information related to the body motion to an acquisition device. The results of a set of experiments we made to assess its performance in real-world setups demonstrate its usefulness in human motion acquisition and tracking, as required, for example, in activity recognition, physical/athletic performance evaluation and rehabilitation.

[1]  Tao Liu,et al.  Gait Analysis Using Wearable Sensors , 2012, Sensors.

[2]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[3]  Tongwen Chen,et al.  Minimizing the Effect of Sampling Jitters in Wireless Sensor Networks , 2011, IEEE Signal Processing Letters.

[4]  Yeh-Liang Hsu,et al.  A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring , 2010, Sensors.

[5]  Shuangquan Wang,et al.  Wearable accelerometer based extendable activity recognition system , 2010, 2010 IEEE International Conference on Robotics and Automation.

[6]  Alex Pentland,et al.  Wearable feedback systems for rehabilitation , 2005, Journal of NeuroEngineering and Rehabilitation.

[7]  Oscar Cordón,et al.  Human Gait Modeling Using a Genetic Fuzzy Finite State Machine , 2012, IEEE Transactions on Fuzzy Systems.

[8]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[9]  M. Paavola,et al.  Wireless monitoring of a steam boiler - performance measurements in industrial environment , 2008, 2008 IEEE International Symposium on Industrial Electronics.

[10]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[11]  Katsunori Ikoma,et al.  Obituary: Yukio Mano (1943–2004) , 2005, Journal of NeuroEngineering and Rehabilitation.

[12]  Luca Mussi,et al.  Wireless human motion acquisition system for rehabilitation assessment , 2012, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS).

[13]  C D Marsden,et al.  Training improves the speed of aimed movements in Parkinson's disease. , 1998, Brain : a journal of neurology.

[14]  Paolo Bonato,et al.  Advances in wearable technology and applications in physical medicine and rehabilitation , 2005, Journal of NeuroEngineering and Rehabilitation.

[15]  D. Flandre,et al.  Miniaturized and low cost innovative detection systems for medical and environmental applications , 2010, 2010 2nd Circuits and Systems for Medical and Environmental Applications Workshop (CASME).

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

[17]  Ingrid Moerman,et al.  A survey on wireless body area networks , 2011, Wirel. Networks.

[18]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[19]  Deborah Estrin,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Fine-grained Network Time Synchronization Using Reference Broadcasts , 2022 .

[20]  Aleksandar Milenkovic,et al.  Wireless sensor networks for personal health monitoring: Issues and an implementation , 2006, Comput. Commun..

[21]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[22]  Robert X. Gao,et al.  Wireless Design of a Multisensor System for Physical Activity Monitoring , 2012, IEEE Transactions on Biomedical Engineering.

[23]  Huosheng Hu,et al.  Inertial measurements of upper limb motion , 2006, Medical and Biological Engineering and Computing.

[24]  Nirmala N Nayak,et al.  Yoga: a therapeutic approach. , 2004, Physical medicine and rehabilitation clinics of North America.

[25]  Paul Lukowicz,et al.  Using acceleration signatures from everyday activities for on-body device location , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[26]  M. Guler,et al.  Measuring and Transmitting Vital Body Signs Using MEMS Sensors , 2007, 2007 1st Annual RFID Eurasia.

[27]  Jin-Shyan Lee,et al.  Performance evaluation of IEEE 802.15.4 for low-rate wireless personal area networks , 2006, IEEE Transactions on Consumer Electronics.

[28]  J. Flanagan,et al.  The reaching movements of patients with Parkinson's disease under self-determined maximal speed and visually cued conditions. , 1998, Brain : a journal of neurology.

[29]  Huosheng Hu,et al.  Use of multiple wearable inertial sensors in upper limb motion tracking. , 2008, Medical engineering & physics.

[30]  Fikret Sivrikaya,et al.  Time synchronization in sensor networks: a survey , 2004, IEEE Network.

[31]  J. D. Janssen,et al.  A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity , 1997, IEEE Transactions on Biomedical Engineering.

[32]  Shyamal Patel,et al.  A review of wearable sensors and systems with application in rehabilitation , 2012, Journal of NeuroEngineering and Rehabilitation.

[33]  Mário Serafim Nunes,et al.  Performance evaluation of IEEE 802.11e , 2002, The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

[34]  Brian L Tracy,et al.  Yoga as Steadiness Training: Effects on Motor Variability in Young Adults , 2008, Journal of strength and conditioning research.