AIMC Topic: Wearable Electronic Devices

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Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices.

Sensors (Basel, Switzerland)
The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) ...

Dynamically predicting comprehension difficulties through physiological data and intelligent wearables.

Scientific reports
Comprehending digital content written in natural language online is vital for many aspects of life, including learning, professional tasks, and decision-making. However, facing comprehension difficulties can have negative consequences for learning ou...

Machine Learning Assisted Electronic/Ionic Skin Recognition of Thermal Stimuli and Mechanical Deformation for Soft Robots.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Soft robots have the advantage of adaptability and flexibility in various scenarios and tasks due to their inherent flexibility and mouldability, which makes them highly promising for real-world applications. The development of electronic skin (E-ski...

Post-stroke hand gesture recognition via one-shot transfer learning using prototypical networks.

Journal of neuroengineering and rehabilitation
BACKGROUND: In-home rehabilitation systems are a promising, potential alternative to conventional therapy for stroke survivors. Unfortunately, physiological differences between participants and sensor displacement in wearable sensors pose a significa...

A portable inflatable soft wearable robot to assist the shoulder during industrial work.

Science robotics
Repetitive overhead tasks during factory work can cause shoulder injuries resulting in impaired health and productivity loss. Soft wearable upper extremity robots have the potential to be effective injury prevention tools with minimal restrictions us...

Motor assessment of X-linked dystonia parkinsonism via machine-learning-based analysis of wearable sensor data.

Scientific reports
X-linked dystonia parkinsonism (XDP) is a neurogenetic combined movement disorder involving both parkinsonism and dystonia. Complex, overlapping phenotypes result in difficulties in clinical rating scale assessment. We performed wearable sensor-based...

AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologis...

Predicting Musculoskeletal Loading at Common Running Injury Locations Using Machine Learning and Instrumented Insoles.

Medicine and science in sports and exercise
INTRODUCTION: Wearables have the potential to provide accurate estimates of tissue loads at common running injury locations. Here we investigate the accuracy by which commercially available instrumented insoles (ARION; ATO-GEAR, Eindhoven, The Nether...

Active Fabrics With Controllable Stiffness for Robotic Assistive Interfaces.

Advanced materials (Deerfield Beach, Fla.)
Assistive interfaces enable collaborative interactions between humans and robots. In contrast to traditional rigid devices, conformable fabrics with tunable mechanical properties have emerged as compelling alternatives. However, existing assistive fa...

A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects.

Nature communications
Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson's disease. During a FOG episode, patients report that their feet are suddenly and inexplicably "glued" to the floo...