Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review was to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, sign language recognition, and human-computer interaction. Results showed that electrical, dynamic, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.
Stroke often leads to hand motor dysfunction, and effective rehabilitation requires keeping patients engaged and motivated. Among the existing automated rehabilitation approaches, data glove-based systems are not easy to wear for patients due to spasticity, and single sensor-based approaches generally provided prohibitively limited information. We thus propose a wearable multimodal serious games approach for hand movement training after stroke. A force myography (FMG), electromyography (EMG), and inertial measurement unit (IMU)-based multi-sensor fusion model was proposed for hand movement classification, which was worn on the user’s affected arm. Two movement recognition-based serious games were developed for hand movement and cognition training. Ten stroke patients with mild to moderate motor impairments (Brunnstrom Stage for Hand II-VI) performed experiments while playing interactive serious games requiring 12 activities-of-daily-living (ADLs) hand movements taken from the Fugl Meyer Assessment. Feasibility was evaluated by movement classification accuracy and qualitative patient questionnaires. The offline classification accuracy using combined FMG-EMG-IMU was 81.0% for the 12 movements, which was significantly higher than any single sensing modality; only EMG, only FMG, and only IMU were 69.6, 63.2, and 47.8%, respectively. Patients reported that they were more enthusiastic about hand movement training while playing the serious games as compared to conventional methods and strongly agreed that they subjectively felt that the proposed training could be beneficial for improving upper limb motor function. These results showed that multimodal-sensor fusion improved hand gesture classification accuracy for stroke patients and demonstrated the potential of this proposed approach to be used as upper limb movement training after stroke.
Exercise is critical to children's mental and physical health. However, improper exercise can be counterproductive. [1] Research shows that excessive exercise can cause stress fractures, ligamentous injuries, and knee articular cartilage damage in children, while lack of exercise can lead to obesity and even depression. [2] Therefore, it is necessary to provide children with professional coaches and health advisers. However, professional guidance is expensive and impractical in rural or underdeveloped areas where health infrastructure is weak and professional coaches are not available. [3] Recently, the issue became more obvious because the coronavirus pandemic quarantined children from their schools' athletic facilities, leaving them with less professional guidance from their teachers. [4] Fortunately, wearable devices (e.g., smartwatches or smart-bands) can provide motion monitoring and exercise advice to people who do not have access to professional coaches and health advisers at a low cost and with good accessibility. [5,6] However, children are often ignored in the design of these devices. Wearable devices and algorithms developed for children lack diversity and function, forcing them to use commercial adult devices or algorithms to get high-quality remote exercise guidance. [7] Utilizing adult devices or algorithms on children faces two severe problems. First, human physical characteristics show variability in different activities. It is hard to build an age group recognition model robust to multiple activities. Second, due to the physiological differences between children and adults, human activity recognition algorithms developed on adults work poorly on children, making motion monitoring inaccurate. [8] Previous research reports a 10.8%-27.1% recognition decrease when using a model trained by adult data to recognize child activity. [9] Providing exercise advice through wearable fitness devices requires three steps: 1) recognizing the user's current activity; 2) monitoring duration or intensity; and 3) providing advice based on the user's age group characteristics and data collected in step 1 and 2. Therefore, incorrect activity recognition and exercise standard misuse will lead to improper exercise advice and can cause excessive exercise or lack of exercise, which would finally lead to harm for the children. [10] In addition, lots of
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.