AIMC Topic: Accelerometry

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Child behavior recognition in social robot interaction using stacked deep neural networks and biomechanical signals.

Scientific reports
With the growing integration of social robots into pediatric environments, understanding and monitoring child-robot interaction has become increasingly important. Toward the advancement of biomechanical monitoring systems for pediatric applications, ...

Exploring parameter optimisation in machine learning algorithms for locomotor task discrimination using wearable sensors.

Scientific reports
The accurate identification of locomotion states from wearable sensor data using machine learning relies heavily on carefully selecting algorithm parameters, which remains a challenging task. This study systematically optimised key parameters-includi...

An accelerometer-based dataset for monitoring slag in steel manufacturing.

BMC research notes
OBJECTIVES: Slag detection in steel manufacturing is essential for ensuring high product quality and process efficiency. The purpose of the accelerometer-based data is to allow for accurate monitoring and differentiation between slag and molten metal...

Smart physical activity monitoring for preventing shoulder impingement syndrome.

Scientific reports
Shoulder impingement syndrome (SIS) is a prevalent musculoskeletal condition requiring effective preventive strategies. This study introduces a comprehensive approach to SIS prevention through smart physical activity monitoring. Wearable sensors, inc...

Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition.

Scientific reports
The recognition of human activity by wearable sensors has garnered significant interest owing to its extensive applications in health, sports, and surveillance systems. This paper presents a novel hybrid deep learning model, termed CNNd-TAm, for the ...

Recognizing Skateboard and Kickboard Commuting Behaviors Using Activity Trackers: Feasibility Study Using Machine Learning Approaches.

JMIR formative research
BACKGROUND: Active commuting, such as skateboarding and kickboarding, is gaining popularity as an alternative to traditional modes of transportation such as walking and cycling. However, current activity trackers and smartphones, which rely on accele...

Belun Sleep Platform versus in-lab polysomnography for obstructive sleep apnea diagnosis.

Sleep & breathing = Schlaf & Atmung
OBJECTIVE: We aimed to compare the Belun Sleep Platform (BSP), an artificial intelligence-driven home sleep testing device, with polysomnography (PSG) for diagnosing obstructive sleep apnea. The BSP analyzes oxygen saturation, heart rate, and acceler...

Benchmarking of open-source algorithms for heart rate estimation from motion-corrupted photoplethysmography.

Computers in biology and medicine
Photoplethysmography holds promise for continuous, non-intrusive heart rate monitoring through wearable devices. However, motion artifacts can impact the reliability of heart rate estimates. The integration of accelerometer data has been proven helpf...

Comparison of machine learning and validation methods for high-dimensional accelerometer data to detect foot lesions in dairy cattle.

PloS one
Lameness is one of the major production diseases affecting dairy cattle. It is associated with negative welfare in affected cattle, economic losses at the farm level, and adverse effects on sustainability. Prompt identification of lameness is necessa...

Energy consumption analysis and prediction in exercise training based on accelerometer sensors and deep learning.

Scientific reports
This study aims to enhance the accuracy and efficiency of energy consumption prediction during exercise training and address the limitations of existing methods in terms of data feature extraction, model complexity, and adaptability to practical appl...