I see nothing here that suggests that protocols were written and placed in a public database that survivors can find and access so this can be presented to their stroke medical 'professionals' to implement. Top down research dissemination does not work, bottom up will work because survivors will demand it be delivered to them.
Home-based guidance training system with interactive visual feedback using kinect on stroke survivors with moderate to severe motor impairment
Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 189 (2024)
Abstract
The home-based training approach benefits stroke survivors by providing them with an increased amount of training time and greater feasibility in terms of their training schedule, particularly for those with severe motor impairment. Computer-guided training systems provide visual feedback with correct movement patterns during home-based training. This study aimed to investigate the improvement in motor performance among stroke survivors with moderate to severe motor impairment after 800 min of training using a home-based guidance training system with interactive visual feedback. Twelve patients with moderate to severe stroke underwent home-based training, totaling 800 min (20–40 min per session, with a frequency of 3 sessions per week). The home-based guidance training system uses Kinect to reconstruct the 3D human body skeletal model and provides real-time motor feedback during training. The training exercises consisted of six core exercises and eleven optional exercises, including joint exercises, balance control, and coordination. Pre-training and post-training assessments were conducted using the Fugl-Meyer Assessment-Upper Limb (FMA-UE), Fugl-Meyer Assessment-Lower Limb (FMA-LE), Functional Ambulation Categories (FAC), Berg Balance Scale (BBS), Barthel Index (BI), Modified Ashworth Scale (MAS), as well as kinematic data of joint angles and center of mass (COM). The results indicated that motor training led to the attainment of the upper limit of functional range of motion (FROM) in hip abduction, shoulder flexion, and shoulder abduction. However, there was no improvement in the active range of motion (AROM) in the upper extremity (U/E) and lower extremity (L/E) joints, reaching the level of the older healthy population. Significant improvements were observed in both left/right and superior/inferior displacements, as well as body sway in the mediolateral axis of the COM, after 800 min of training. In conclusion, the home-based guidance system using Kinect aids in improving joint kinematics performance at the level of FROM and balance control, accompanied by increased mediolateral body sway of the COM for stroke survivors with moderate to severe stroke. Additionally, spasticity was reduced in both the upper and lower extremities after 800 min of home-based training.
Introduction
Existing home-based training systems use games as the primary trend to enhance the level of motivation [1,2,3,4,5]. In addition, the training efficiency, in terms of repetitions, may be lower when utilizing the home-based training system without feedback [6]. If the fun environment is enriched and the correct movement is guided by visual feedback during training exercises, this approach would be a feasible training approach. Cloud computing technology has advanced recently, cloud-based networks can easily connect the training from home to the center, and therapists can adjust the training protocol remotely and access the training data. Two markerless systems for motion tracking 3D depth sensor technique and RGB camera system. 3D depth-sensing cameras, employing technologies such as stereo vision, time of flight, or structured light, are now capable of identifying 3D body segments. Notable examples include Kinect [7,8,9,10,11], ZED [12, 13], Intel RealSense [14]. Another type of system using markerless’s AI-driven motion capture technology with RGB cameras (e.g., Theia3D) [15] to construct the 3D skeletal models for tracking joint movements and balance control. In this work, we raise the following questions: Is it possible to have an interactive guidance system for home-based users during training, and what is the effectiveness of motor recovery after 800 min of home-based training using computer-guided visual feedback?
The home-based training approach [16,17,18] provides training for chronic stroke and benefits stroke survivors with an increased amount of training time and more feasibility in the training schedule, especially in patients with severe levels. The depth sensor-based training systems (i.e., UINCARE Home + (UINCARE Corp., South Korea) [19, 20], MindMotion® GO (Switzerland) [21], EvolvRehab (Spain) [22], LongGood TeleRehabilitation System (Taiwan) [23]) provide the visual feedback with specific symbols or targets to guide the user in achieving the tasks. However, these training approaches display virtual objects in a video game or demonstration videos without providing feedback on the user’s real-time motions. In this study, we developed a home-based guidance training system for stroke survivors, enabling to provide the RGB-depth sensor to capture 25 artificial anatomical landmarks to reconstruct the body skeleton and real-time visual feedback on users' body segment movements and joint angles during training.
The purpose of this study was to investigate the improvement in motor performance for stroke survivors with moderate to severe motor impairment after 800 min of training using the home-based guidance training system with interactive visual feedback. With the technology of depth sensors, home-based training with computer-guided motion guidance in real-time may help users improve motor performance with minimal therapist assistance, which can greatly enhance the flexibility to facilitate their training time schedule.
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