Purpose: The study aimed to explore how children with cerebral palsy (CP) perceived their disability and assistive devices and to consider the factors influencing their device use in home and school settings.
Method: Semi-structured interviews were adopted as the main data collection instrument. There were 44 participants, which comprised of 15 Taiwanese children with CP as well as their mothers and teachers.
Results: Most children associated their perceptions of disability with their experiences of lower physical performance. Consequently, they generally perceived assistive devices as having a positive effect on their disability. Their enthusiasm for using their devices in the home and school contexts, however, was markedly different. Four factors leading to such a difference were identified, namely the nature of the two environments, physical environmental factors, the children's desired level of independence and the mothers' attitudes.
Conclusions: The results demonstrate the significance of child-environment interaction. The children's attitudes towards device usage are influenced by their perceptions of the contextual feature of both settings. Additionally, the results indicate that children's views about their assistive devices may be different from those of adult users due to their different developmental stages and unique personal experiences. The findings suggest the importance of children's active participation in the field of assistive device research.
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http://dx.doi.org/10.1080/17483100802613701 | DOI Listing |
Appl Ergon
January 2025
University Savoie Mont Blanc, Interuniversity Laboratory of Human Movement Sciences, Le Bourget du Lac, F-7337, France. Electronic address:
Home care workers are affected by musculoskeletal disorders caused by biomechanical factors. This study investigated the effect of three exoskeletons devices (HAPO, HAPO FRONT and Japet.W) during load mobilization tasks at three bed heights in order to reduce physical risk factor.
View Article and Find Full Text PDFJ Prosthodont Res
January 2025
Department of Prosthodontics, Showa University School of Dentistry, Tokyo, Japan.
Purpose: This study examined the effect of assistive device use on the precision of digital impressions for multiple implants placed in a fully edentulous maxilla in vivo.
Methods: A total of eight participants with fully edentulous maxillae and four implants at position #15, #12, #22, and #25 were included in the study. The assistive device was made using CAD/CAM technology.
J Mot Behav
January 2025
Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Numerous devices are being developed to assist visually impaired and blind individuals in performing everyday tasks such as reaching out to grasp objects. Considering that the size, weight, and cost of assistive devices significantly impact their acceptance, it would be useful to know how effective various types of guiding information can be. As an initial exploration of this issue, we conducted four studies in which participants with normal vision were visually guided toward targets.
View Article and Find Full Text PDFGait Posture
January 2025
School of Engineering Medicine, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China. Electronic address:
Background: The clinical benefits and widespread use of traditional mobility aids (such as canes, walking frames, wheeled walkers, etc.) have been hampered by improper use, fear of falling, and social stigma. Clarifying the biomechanical impacts of using mobility aids on users is fundamental to optimizing rehabilitation programs.
View Article and Find Full Text PDFGait Posture
January 2025
Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA; Department of Surgery and Research Service, Nebraska-Western Iowa Veterans Affairs Medical Center, Omaha, NE 68105, USA. Electronic address:
Background: This study leverages Artificial Neural Networks (ANNs) to predict lower limb joint moments and electromyography (EMG) signals from Ground Reaction Forces (GRF), providing a novel perspective on human gait analysis. This approach aims to enhance the accessibility and affordability of biomechanical assessments using GRF data, thus eliminating the need for costly motion capture systems.
Research Question: Can ANNs use GRF data to accurately predict joint moments in the lower limbs and EMG signals?
Methods: We employed ANNs to analyze GRF data and to use them to predict joint moments (363-trials; 4-datasets) and EMG signals (63-trials; 2-datasets).
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