Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets.

Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets-the camera-based method (Vicon) and wearable sensor-based technology (Xsens)-were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors.

Comparison of proximal humeral bone stresses between stemless, short stem, and standard stem length: a finite element analysis.

BACKGROUND: The stem lengths of humeral components used in shoulder arthroplasty vary; however, the literature on these devices is limited. This finite element study investigates the effect of humeral component length on stresses in the proximal humerus. METHODS: Intact and 3 reconstructed (standard length, short, and stemless implants) finite element models were created from shoulder computed tomography scan data (N = 5). Loading was simulated at varying abduction angles (15°, 45°, and 75°). The average bone stress (represented as a percentage of intact values) was reported at 8 transverse slices. In addition, the overall average change in cortical and trabecular bone stresses was quantified. RESULTS: Cortical bone stresses in the most proximal slice for the standard (58% ± 12%) and short (78% ± 10%) stem models were significantly reduced compared with the intact (100%) and stemless (101% ± 6%) models (P = .005). These reductions persisted in the second cortical slice for the standard stem compared with the intact, stemless, and short models (P = .025). Interestingly, stresses in the trabecular bone within these proximal slices were significantly elevated when stemless implants were used compared with all other implants (P < .001), regardless of abduction angle. CONCLUSION: Reducing stem length produced humeral stresses that more closely matched the intact stress distribution in proximal cortical bone. Opposing trends presented in the proximal trabecular bone, probably because of differences in load transfer when shorter stems are used. Accordingly, the results suggest that implant stem length is 1 variable that can be modified in an attempt to better mimic intact bone stresses during humeral component insertion, provided stem fixation is adequate.

Development and feasibility of a soft pneumatic-robotic glove to assist impaired hand function in quadriplegia patients: A pilot study.

One of the common disorders in people with quadriplegia is having a weak grip strength that can affect activities of daily living (ADL). This study presents the design of a soft robotic glove via pneumatic actuators and feasibility according to a range of motion (ROM) of proximal interphalangeal (PIP) joint and user friendly. The soft robotic glove includes a neoprene cockup, two pumps, a controller that adjusts the pressure of the pumps, two-direction parts, and two silicone tubes placed on an index and middle fingers. A total of seven subjects (2healthy, 5quadriplegia patients) participated in this project. Performance of the device was verified through assessment in healthy participants first and then spinal cord injury (SCI) participants. The device evaluated the range of motion (ROM) of a proximal interphalangeal (PIP) joint. Then, subjects completed a satisfaction questionnaire. Results showed the ROM of the PIP joint (p value = 0.042) increased by using the robotic glove. The average score of the satisfaction questionnaire was 4.24 which was beyond the desirable threshold. In conclusion, the glove obtained ROM requirements to the grip usual objects and underlined the potential for assisting SCI participants in ADLs. Providing motion in all fingers should be investigated and developed in the future.