AIMC Topic: Accelerometry
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Freezing of gait detection: The effect of sensor type, position, activities, datasets, and machine learning model.
Journal of Parkinson's disease
Mar 16, 2025
BackgroundFreezing of gait (FoG) is a complex, frequent, and disabling motor symptom of Parkinson's disease (PD). Wearable technology has the potential to improve FoG assessment by providing objective, quantitative, and continuous monitoring.Objectiv...
A Comparative Study of Plantar Pressure and Inertial Sensors for Cross-Country Ski Classification Using Deep Learning.
Sensors (Basel, Switzerland)
Feb 28, 2025
This work presents a comparative study of low cost and low invasiveness sensors (plantar pressure and inertial measurement units) for classifying cross-country skiing techniques. A dataset was created for symmetrical comparative analysis, with data c...
Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity Recognition.
Sensors (Basel, Switzerland)
Feb 14, 2025
BACKGROUND: Human activity recognition (HAR) plays a pivotal role in digital healthcare, enabling applications such as exercise monitoring and elderly care. However, traditional HAR methods relying on accelerometer data often require complex preproce...
A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies.
BMC medical informatics and decision making
Feb 11, 2025
This study proposes a deep learning-based motion assessment method that integrates the pose estimation algorithm (Keypoint RCNN) with signal processing techniques, demonstrating its reliability and effectiveness.The reliability and validity of this m...
Human sleep position classification using a lightweight model and acceleration data.
Sleep & breathing = Schlaf & Atmung
Feb 10, 2025
PURPOSE: This exploratory study introduces a portable, wearable device using a single accelerometer to monitor twelve sleep positions. Targeted for home use, the device aims to assist patients with mild conditions such as gastroesophageal reflux dise...
Step Width Estimation in Individuals With and Without Neurodegenerative Disease via a Novel Data-Augmentation Deep Learning Model and Minimal Wearable Inertial Sensors.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Step width is vital for gait stability, postural balance control, and fall risk reduction. However, estimating step width typically requires either fixed cameras or a full kinematic body suit of wearable inertial measurement units (IMUs), both of whi...
Towards the automatic detection of activities of daily living using eye-movement and accelerometer data with neural networks.
Computers in biology and medicine
Jan 1, 2025
Early diagnosis of neurodegenerative diseases, such as Alzheimer's disease, improves treatment and care outcomes for patients. Early signs of cognitive decline can be detected using functional scales, which are written records completed by a clinicia...
Automatic identification of the endangered hawksbill sea turtle behavior using deep learning and cross-species transfer learning.
The Journal of experimental biology
Dec 23, 2024
The accelerometer, an onboard sensor, enables remote monitoring of animal posture and movement, allowing researchers to deduce behaviors. Despite the automated analysis capabilities provided by deep learning, data scarcity remains a challenge in ecol...
Use of Hearing Aids Embedded with Inertial Sensors and Artificial Intelligence to Identify Patients at Risk for Falling.
Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
Dec 17, 2024
OBJECTIVE: To compare fall risk scores of hearing aids embedded with inertial measurement units (IMU-HAs) and powered by artificial intelligence (AI) algorithms with scores by trained observers.
Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology.
Sensors (Basel, Switzerland)
Dec 14, 2024
Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural networ...
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