Abstract
BackgroundEmerging sensing and communication technologies are contributing to the development of many motor rehabilitation programs outside the standard healthcare facilities. Nowadays, motor rehabilitation exercises can be easily performed and monitored even at home by a variety of motion-tracking systems. These are cheap, reliable, easy-to-use, and allow also remote configuration and control of the rehabilitation programs. The two most promising technologies for home-based motor rehabilitation programs are inertial wearable sensors and video-based motion capture systems.MethodsIn this paper, after a thorough review of the relevant literature, an original experimental analysis is reported for two corresponding commercially available solutions, a wearable inertial measurement unit and the Kinect, respectively. For the former, a number of different algorithms for rigid body pose estimation from sensor data were also tested. Both systems were compared with the measurements obtained with state-of-the-art marker-based stereophotogrammetric motion analysis, taken as a gold-standard, and also evaluated outside the lab in a home environment.ResultsThe results in the laboratory setting showed similarly good performance for the elementary large motion exercises, with both systems having errors in the 3–8 degree range. Usability and other possible limitations were also assessed during utilization at home, which revealed additional advantages and drawbacks for the two systems.ConclusionsThe two evaluated systems use different technology and algorithms, but have similar performance in terms of human motion tracking. Therefore, both can be adopted for monitoring home-based rehabilitation programs, taking adequate precautions however for operation, user instructions and interpretation of the results.
Highlights
Emerging sensing and communication technologies are contributing to the development of many motor rehabilitation programs outside the standard healthcare facilities
This is mainly due to a combination of the type of performed exercises and their short duration, which allow ACC or Gyroscope integration (GYR) estimates to have limited Root Mean Square Error (RMSE)
The Inertial measurement unit (IMU) estimates employing sensor fusion algorithms outperform the Kinect’s output, though by a limited margin, revealing that both approaches have a good overall performance, with errors in the range of 3 to 8 degree for all the joint angles analyzed, which is consistent with existing literature [39, 55, 59,60,61, 68]
Summary
Emerging sensing and communication technologies are contributing to the development of many motor rehabilitation programs outside the standard healthcare facilities. Major drivers of healthcare innovation include the priority changes from treatment to Milosevic et al BioMed Eng OnLine (2020) 19:25 prevention, and the search to provide personalized and patient-centric solutions Both trends are enabled by unobtrusive sensing technologies, allowing for continuous monitoring and increased engagement with the patient outside the clinic [2]. The development of miniaturized inertial sensors paved the way for the development of wearable Inertial Measurement Units (IMUs) and their use for motion capture [6, 7] Such technologies have been validated in lab environments for medical applications and motor rehabilitation analyses [8, 9]; the available solutions involve cost and complexityrelated limitations
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