Evaluating the Performance of Balance Physiotherapy Exercises Using a Sensory Platform: The Basis for a Persuasive Balance Rehabilitation Virtual Coaching System

Vassilios D Tsakanikas, Dimitrios Gatsios, Dimitrios Dimopoulos, Athanasios Pardalis, Marousa Pavlou, Matthew B Liston, Dimitrios I Fotiadis, Vassilios D Tsakanikas, Dimitrios Gatsios, Dimitrios Dimopoulos, Athanasios Pardalis, Marousa Pavlou, Matthew B Liston, Dimitrios I Fotiadis

Abstract

Rehabilitation programs play an important role in improving the quality of life of patients with balance disorders. Such programs are usually executed in a home environment, due to lack of resources. This procedure usually results in poorly performed exercises or even complete drop outs from the programs, as the patients lack guidance and motivation. This paper introduces a novel system for managing balance disorders in a home environment using a virtual coach for guidance, instruction, and inducement. The proposed system comprises sensing devices, augmented reality technology, and intelligent inference agents, which capture, recognize, and evaluate a patient's performance during the execution of exercises. More specifically, this work presents a home-based motion capture and assessment module, which utilizes a sensory platform to recognize an exercise performed by a patient and assess it. The sensory platform comprises IMU sensors (Mbientlab MMR© 9axis), pressure insoles (Moticon©), and a depth RGB camera (Intel D415©). This module is designed to deliver messages both during the performance of the exercise, delivering personalized notifications and alerts to the patient, and after the end of the exercise, scoring the overall performance of the patient. A set of proof of concept validation studies has been deployed, aiming to assess the accuracy of the different components for the sub-modules of the motion capture and assessment module. More specifically, Euler angle calculation algorithm in 2D (R 2 = 0.99) and in 3D (R 2 = 0.82 in yaw plane and R 2 = 0.91 for the pitch plane), as well as head turns speed (R 2 = 0.96), showed good correlation between the calculated and ground truth values provided by experts' annotations. The posture assessment algorithm resulted to accuracy = 0.83, while the gait metrics were validated against two well-established gait analysis systems (R 2 = 0.78 for double support, R 2 = 0.71 for single support, R 2 = 0.80 for step time, R 2 = 0.75 for stride time (WinTrack©), R 2 = 0.82 for cadence, and R 2 = 0.79 for stride time (RehaGait©). Validation results provided evidence that the proposed system can accurately capture and assess a physiotherapy exercise within the balance disorders context, thus providing a robust basis for the virtual coaching ecosystem and thereby improve a patient's commitment to rehabilitation programs while enhancing the quality of the performed exercises. In summary, virtual coaching can improve the quality of the home-based rehabilitation programs as long as it is combined with accurate motion capture and assessment modules, which provides to the virtual coach the capacity to tailor the interaction with the patient and deliver personalized experience.

Keywords: IMU sensors; balance disorders; gait analytics; motion capture; motor score; persuasive technology; physiotherapy exercises; virtual coach.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Tsakanikas, Gatsios, Dimopoulos, Pardalis, Pavlou, Liston and Fotiadis.

Figures

Figure 1
Figure 1
Human motion tracking technologies.
Figure 2
Figure 2
Virtual coaching closed-loop interaction.
Figure 3
Figure 3
Graph G for (A) normal and (B) abnormal gait.
Figure 4
Figure 4
Experimental setups for validating IMU data. (A) 2D validation setup and (B) 3D validation setup.
Figure 5
Figure 5
Experimental setups for validating gait parameters: (A) Comparison with the WinTrack© system, and (B) comparison with the RehaGait© system).
Figure 6
Figure 6
Proposed methodology for exercise scoring.
Figure 7
Figure 7
Motion capture and exercise assessment module setup.
Figure 8
Figure 8
Comparison between observed and calculated angles in (A) yaw and (B) pitch planes.
Figure 9
Figure 9
Bland–Altman plots for yaw (A) and pitch (B) planes.
Figure 10
Figure 10
Bland–Altman analysis for gait parameters: (A) CoPx-WinTrack© as reference, (B) Max pressure-WinTrack© as reference, (C) Double support-WinTrack© as reference, (D) Single support-WinTrack© as reference, (E) Stride time-WinTrack© as reference, (F) Step time-WinTrack© as reference, (G) Cadence-RehaGait© as reference, and (H) Stride time-RehaGait© as reference.
Figure 11
Figure 11
Validation results for the scoring functions: (A) Online scoring and (B) offline scoring.

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