A home-based rehabilitation system for deficient knee patients
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The Smart Health paradigm has opened up immense possibilities for designing cyber-physical systems with integrated sensing and analysis for data-driven healthcare decision-making. Clinical motor-rehabilitation has traditionally tended to entail labor-intensive approaches with limited quantitative methods and numerous logistics deployment challenges. We believe such labor-intensive rehabilitation procedures offer a fertile application field for robotics and automation technologies which is easily applicable to home-based rehabilitation system. Our long-term goal is the creation, analysis and validation of a Home-based Rehabilitation Framework comprised of quantitative human subject measurement technologies, an adjustable smart brace coupled with an integrated PC-based control system to enhance rehabilitation process for deficient knee patient. Human motion-capture and computational analysis tools have played a significant role in a variety of product-design and ergonomics settings for over a quarter-century. However, there exist significant differences in the capabilities and ease-of-use between these tools thus we perform comparative analysis of motion data from two alternate human motion-capture systems (high-resolution Vicon vs low-resolution Kinect). In addition to traditional resolution/accuracy study, data for multiple trials of motions were captured and examined to verify motion capture fidelity and the role of pre- and post-processing (calibration and estimation). In our work, we adapt Principal Component Analysis (PCA) approaches and K-Nearest Neighbors (K-NN) method for subject classification. Knee bracing has been used to realize a variety of functional outcomes in both sport and rehabilitation application. Traditionally, the design of exoskeletons (from choice of configuration to selection of parameters) as well as the process of fitting this exoskeleton (to the individual user/patient) has largely depended on intuition and/or practical experience of a designer/physiotherapist. However, improper exoskeleton design and/or incorrect fitting can cause buildup of significant residual forces/torques (both at joint and fixation site). Performance can be further compromised by the innate complexity of human motions and need to accommodate the immense individual variability (in terms of patient-anthropometrics, motion-envelopes and musculoskeletal-strength). In our work, we propose a systematic and quantitative methodology to evaluate various alternate exoskeleton designs using kinetostatic design optimization and twist-/wrench- based modeling and analysis. This process is applied in the context of a case-study for developing optimal configuration and fixation of a knee brace/exoskeleton. An optimized knee brace is prototyped using 3D printing and physically tested. Recent research on exoskeletons has examined ways of improving flexibility, wearability as well as reducing overall weight. Very few exoskeletal systems, however, have succeeded in satisfying all these criteria due to the complexities engaged in human joint motions and loading. Compliant mechanisms offer a class of articulated multibody systems that allow relatively stiff but lightweight solutions for exoskeleton/braces. In our study, we introduce Parallel Coupled Compliant plate (PCCP) mechanism and Pennate Elastic Band (PEB) spring architecture and evaluate them. PCCP/PEB system provides both flexibility and extreme stiffness to user with respect to posture/angle of knee joint. The performance of PCCP/PEB system was verified by 3D printed physical exoskeleton prototype. The overall human subject measurement and adjustable smart brace controller are integrated within a Matlab based acquisition, analysis and control framework. Motion measured by low-cost devices (Kinect and Wii Balance Board) was used to calculate load at knee joint then the smart knee brace automatically adjusted parameters of brace to control load at the knee joint based on prescription by therapist or doctor.