A telehealth system framework for assessing knee-joint conditions using vibroarthrographic signals

In this study, we propose a system which encodes, analyses and segments actigraphy-based VAG data for assessing the severity of cartilage degeneration, and highlighting instances of activity which causes crackling sounds during limb movements. The proposed system encapsulates IoMT (Internet of Medical Things) requirements by providing efficient data compression and analysis at the source. Using an actigraphy dataset from 89 participants, our experiments yielded that the system is able to compress the actigraphy data into 3-bits per sample, thereby reducing the signal size by about 88%, without losing any vital limb movement information. This has been further validated using a simple pattern classification of 3-bit quantized VAG data from participants with healthy and unhealthy knee joints. The method, which yielded a recognition accuracy of 84.6%, as compared to raw VAG data which yielded 80% accurate results. In addition, the proposed adaptive segmentation scheme in the system, leverages the 3-bit encoding by correctly identifying 90% of the segments of interest from each VAG signal.
Source: Biomedical Signal Processing and Control - Category: Biomedical Science Source Type: research