Inertial based hand position tracking for future applications in rehabilitation environments

This work is about the application of a wireless and miniaturized MEMS based Attitude and Heading Reference System for the estimation of hands position during standard rehabilitation exercises. The 3D orientation of the platform, computed on-board, along with the acceleration data, are collected by a computer. A specific algorithm has been developed in order to provide a reliable 3D position tracking of the hand without suffering from common error sources of MEMS sensors data processing, such as integration drift, inaccurate calibration procedures and finite integration times. This paper presents the setup, the developed algorithm and the preliminary results achieved with both a mechanical arm and a set of standard physical exercises performed by a human.

[1]  Robert Harle,et al.  A Survey of Indoor Inertial Positioning Systems for Pedestrians , 2013, IEEE Communications Surveys & Tutorials.

[2]  Björn Eskofier,et al.  Performance Comparison of Two Step Segmentation Algorithms Using Different Step Activities , 2014, 2014 11th International Conference on Wearable and Implantable Body Sensor Networks.

[3]  Andrea Vitali,et al.  Development of a wireless low-power multi-sensor network for motion tracking applications , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[4]  Daniele Comotti,et al.  A Novel Body Sensor Network for Parkinson's Disease Patients Rehabilitation Assessment , 2014, 2014 11th International Conference on Wearable and Implantable Body Sensor Networks.

[5]  Majid Sarrafzadeh,et al.  Automated Wolf Motor Function Test (WMFT) for Upper Extremities Rehabilitation , 2012, 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks.

[6]  Daniele Comotti,et al.  neMEMSi: One step forward in wireless attitude and heading reference systems , 2014, 2014 International Symposium on Inertial Sensors and Systems (ISISS).

[7]  Muhammad Mahadi Abdul Jamil,et al.  Multi-sensor arm rehabilitation monitoring device , 2012 .

[8]  I-Ming Chen,et al.  Human velocity and dynamic behavior tracking method for inertial capture system , 2012 .

[9]  R. Riener,et al.  Increasing motivation in robot-aided arm rehabilitation with competitive and cooperative gameplay , 2014, Journal of NeuroEngineering and Rehabilitation.

[10]  Noel E. O'Connor,et al.  Automatic Activity Classification and Movement Assessment During a Sports Training Session Using Wearable Inertial Sensors , 2014, 2014 11th International Conference on Wearable and Implantable Body Sensor Networks.

[11]  Gianluigi Ferrari,et al.  Linking UPDRS Scores and Kinematic Variables in the Leg Agility Task of Parkinsonians , 2014, 2014 11th International Conference on Wearable and Implantable Body Sensor Networks.

[12]  Yong Yan,et al.  Quantitative Assessment of Upper Limb Motion in Neurorehabilitation Utilizing Inertial Sensors , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.