LESIM designed and implemented a motion tracking system relying on wearable Body Area Sensor Network (BASN) technology that can be deployed in domestic environment as easily as on sports ground, thanks to wireless transmission that doesn't constrain the subject's mobility: the patient can keep doing their daily activities while being treated with home rehabilitation therapies, the athlete can monitor (not affecting) their performance during practise. As well as being battery-embedded requiring no bulky battery pack and wire running along the body, sensor nodes are as small as it takes to assure no impact on performance.

Sensed data are collected and delivered by a small gateway, and, once processed, they allow trainers, therapists or the subjects themselves to watch “live” or delayed playback of the whole motor behavior from their workstations. By means of this system, measurements are made available both for real-time and offline purposes, such that analysts can be assisted in their work and take advantage of the possibility to reason about objective and comparable quantities.

Given an articulation to monitor, both of the involved limb segments are provided with a sensor node, mounted with an inertial/magnetic measurement unit: 3-axis accelerometer sensing linear acceleration, 3-axis gyroscope measuring angular rates, and 3-axis magnetometer intended to sense the Earth magnetic field.

Unlike those that keep track of how much activity one has, the designed system allows to assess complex motor skills by monitoring the whole-body motion. Analysts can watch “live” or delayed playback, augmented with related real-time measures. Once the motion of all single joints of interest has been captured, the system evaluates the action resulting from combined movements. For instance, it can assess the motion smoothness of ankles, knees, hips, wrists, elbows and shoulders for a tennis player during a downswing, in order to provide helpful hints on what body parts to focus workout on for improving their serve.

The system is quickly deliverable on the pitch or at home as the sensors are capable to detect automatically how they are being worn. As a consequence, setup is easy and requires no assistance from anyone else: athletes can be trainer-independent as they are free to put sensors on by themselves and go play. At the end of the session, captured performance is there to be analyzed.

Measurements obtained under dynamic conditions have been validated versus those resulting from commercial solutions, specifically designed for assisting motor dysfunction treatment at specialized rehabilitation centers.

References

• P. Daponte, L. De Vito, and C. Sementa, “A Wireless-based Home Rehabilitation System for Monitoring 3D Movements,” in Procs. of IEEE International Symposium on Medical Measurement and Applications, 2013. Gatineau (Canada), pp. 282–287.
• P. Daponte, L. De Vito, and C. Sementa, “Validation of a Home Rehabilitation System for Range of Motion Measurements of Limb Functions,” in Procs. of IEEE International Symposium on Medical Measurement and Applications, 2013. Gatineau (Canada), pp. 288–293.
• P. Daponte, L. De Vito, M. Riccio, and C. Sementa, “Design and validation of a motion-tracking system for ROM measurements in home rehabilitation,” Measurement, vol. 55, no. 9, pp. 82–96, Sep. 2014.
• P. Daponte, L. De Vito, M. Riccio, and C. Sementa, “Experimental Comparison of Orientation Estimation Algorithms in Motion Tracking for Rehabilitation,” in Procs. of IEEE International Symposium on Medical Measurement and Applications, 2014, Lisbon (Portugal), pp. 153–158.
• P. Daponte, L. De Vito, G. Mazzilli, S. Rapuano and C. Sementa, “Investigating the On-board Data Processing for IMU-based Sensors in Motion Tracking for Rehabilitation,” in Procs. of IEEE International Symposium on Medical Measurement and Applications, 2015, Turin (Italy), pp. 645-650.
• P. Daponte, L. De Vito, S. Rapuano, M. Riccio and F. Picariello, “Compensating magnetic disturbances on MARG units by means of a low complexity data fusion algorithm,” in Procs. of IEEE International Symposium on Medical Measurement and Applications, 2015, Turin (Italy), pp. 157-162.
• L. De Vito, G. Mazzilli, M. Riccio, C. Sementa, "Improving the Orientation Estimation in a Packet Loss-affected Wireless Sensor Network for Tracking Human Motion", Proceedings of 21th IMEKO International Symposium on Understanding the World through Electrical and Electronic Measurement, Budapest, Hungary, September 7-9, 2016