OBJECTIVE: The objective of this study was to identify gait alterations related to worsening knee pain and worsening physical function, using machine learning approaches applied to wearable sensor-derived data from a large observational cohort.
Human Activity Recognition (HAR) refers to a field that aims to identify human activities by adopting multiple techniques. In this field, different applications, such as smart homes and assistive robots, are introduced to support individuals in their...
Egocentric activity recognition is a prominent computer vision task that is based on the use of wearable cameras. Since egocentric videos are captured through the perspective of the person wearing the camera, her/his body motions severely complicate ...
BACKGROUND: Diabetic neuropathy is one of the most common complications of diabetes mellitus. The aim of this study is to evaluate the Moveo device, a novel device that uses a machine learning (ML) algorithm to detect and track diabetic neuropathy. T...
Empowering robots with tactile perception and even thinking as well as judgment capabilities similar to those of humans is an inevitable path for the development of future robots. Here, we propose a biomimetic electronic skin (BES) that truly serves ...
Smart wearables with the capability for continuous monitoring, perceiving, and understanding human tactile and motion signals, while ensuring comfort, are highly sought after for intelligent healthcare and smart life systems. However, concurrently ac...
The flexible bimodal e-skin exhibits significant promise for integration into the next iteration of human-computer interactions, owing to the integration of tactile and proximity perception. However, those challenges, such as low tactile sensitivity,...
OBJECTIVE: To evaluate whether a machine-learning algorithm (ie, the "NightSignal" algorithm) can be used for the detection of postoperative complications before symptom onset after cardiothoracic surgery.
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