AIMC Topic: Wearable Electronic Devices

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Comparing stress prediction models using smartwatch physiological signals and participant self-reports.

Computer methods and programs in biomedicine
Recent advances in wearable technology have facilitated the non-obtrusive monitoring of physiological signals, creating opportunities to monitor and predict stress. Researchers have utilized machine learning methods using these physiological signals ...

Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition.

Sensors (Basel, Switzerland)
As many as 40% to 50% of patients do not adhere to long-term medications for managing chronic conditions, such as diabetes or hypertension. Limited opportunity for medication monitoring is a major problem from the perspective of health professionals....

Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations.

PloS one
Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work acti...

Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis.

IEEE journal of biomedical and health informatics
Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments...

Is machine learning and automatic classification of swimming data what unlocks the power of inertial measurement units in swimming?

Journal of sports sciences
Researchers have heralded the power of inertial sensors as a reliable swimmer-centric monitoring technology, however, regular uptake of this technology has not become common practice. Twenty-six elite swimmers participated in this study. An IMU (100H...

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study.

JMIR mHealth and uHealth
BACKGROUND: The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstr...

Well-rounded devices: the fabrication of electronics on curved surfaces - a review.

Materials horizons
With the arrival of the internet of things and the rise of wearable computing, electronics are playing an increasingly important role in our everyday lives. Until recently, however, the rigid angular nature of traditional electronics has prevented th...

Development of the Ultralight Hybrid Pneumatic Artificial Muscle: Modelling and optimization.

PloS one
Pneumatic artificial muscles (PAMs) are one of the key technologies in soft robotics, and they enable actuation in mobile robots, in wearable devices and exoskeletons for assistive and rehabilitative purposes. While they recently showed relevant impr...

Rugged and Compact Three-Axis Force/Torque Sensor for Wearable Robots.

Sensors (Basel, Switzerland)
In the field of robotics, sensors are crucial in enabling the interaction between robots and their users. To ensure this interaction, sensors mainly measure the user's strength, and based on this, wearable robots are controlled. In this paper, we pro...

Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III.

Biomedical engineering online
BACKGROUND: Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson's disease (PD) motor complications. Wearable technologies could be used to reduce the ne...