In this work, we present a perspective on the role machine intelligence can play in supporting human abilities. In particular, we consider research in rehabilitation technologies such as prosthetic devices, as this domain requires tight coupling between human and machine. Taking an agent-based view of such devices, we propose that human-machine collaborations have a capacity to perform tasks which is a result of the combined agency of the human and the machine. We introduce as a resource developed by a human and a machine working together in ongoing interactions. Development of this resource enables the partnership to eventually perform tasks at a capacity greater than either individual could achieve alone. We then examine the benefits and challenges of increasing the agency of prostheses by surveying literature which demonstrates that building communicative resources enables more complex, task-directed interactions. The viewpoint developed in this article extends current thinking on how best to support the functional use of increasingly complex prostheses, and establishes insight toward creating more fruitful interactions between humans and supportive, assistive, and augmentative technologies.
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http://dx.doi.org/10.1007/s00521-022-07948-1 | DOI Listing |
ACS Appl Mater Interfaces
January 2025
Centre for Robotics and Automation, Department of Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, China.
Liquid metals are highly conductive like metallic materials and have excellent deformability due to their liquid state, making them rather promising for flexible and stretchable wearable sensors. However, patterning liquid metals on soft substrates has been a challenge due to high surface tension. In this paper, a new method is proposed to overcome the difficulties in fabricating liquid-state strain sensors.
View Article and Find Full Text PDFTherapies against hematological malignancies using chimeric antigen receptors (CAR)-T cells have shown great potential; however, therapeutic success in solid tumors has been constrained due to limited tumor trafficking and infiltration, as well as the scarcity of cancer-specific solid tumor antigens. Therefore, the enrichment of tumor-antigen specific CAR-T cells in the desired region is critical for improving therapy efficacy and reducing systemic on-target/off-tumor side effects. Here, we functionalized human CAR-T cells with superparamagnetic iron oxide nanoparticles (SPIONs), making them magnetically controllable for site-directed targeting.
View Article and Find Full Text PDFWorld J Clin Cases
January 2025
Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece.
Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the .
View Article and Find Full Text PDFCureus
December 2024
Otolaryngology, Imperial College London Healthcare National Health Service (NHS) Trust, London, GBR.
We report a case of a 45-year-old gentleman who presented to our major trauma centre after sustaining a penetrating high-pressure paint injection injury to the neck. This rare mechanism of injury is most commonly reported to affect the non-dominant hand, occurring due to the malfunction or misuse of industrial paint machines, causing a piercing soft tissue injury with high-pressure fluid. The unique challenges faced in managing penetrating injuries to the neck are due to the density of vital visceral structures in the region, including major blood vessels and the upper aerodigestive tract.
View Article and Find Full Text PDFFront Bioeng Biotechnol
December 2024
Department of Rehabilitation Medicine, University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
Introduction: Parkinson's disease (PD) is characterized by muscle stiffness, bradykinesia, and balance disorders, significantly impairing the quality of life for affected patients. While motion pose estimation and gait analysis can aid in early diagnosis and timely intervention, clinical practice currently lacks objective and accurate tools for gait analysis.
Methods: This study proposes a multi-level 3D pose estimation framework for PD patients, integrating monocular video with Transformer and Graph Convolutional Network (GCN) techniques.
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