Attention-based CNN-LSTM architecture for smartphone IMU-based human activity recognition in intelligent mobile monitoring systems

Deep Learning Approaches for Recognizing Human Activity using Sensor Data

These results demonstrate the models’ respective strengths in capturing long-range temporal dependencies, bidirectional temporal patterns, and short-term temporal dynamics with cross-channel interactions in complex motion patterns.

An attention-driven recurrent network and vision transformer-based assistive model for disability-oriented human activity recognition

A Deep Multiple Instance Multiple Label Learning Framework for Smartphone-Based Human Activity Recognition

Human Activity Recognition through smartphones plays a crucial role in several medical states of affairs such as patient monitoring, elder-care, and post-surgery recovery. Most of these works require precisely labelled smartphone accelerometer data for training supervised learning classifiers. However, precise data ann...

Human Activity Recognition at the Edge: A Systematic Review of Machine Learning Models, Deployment Constraints, and Latency Energy Accuracy Trade offs

The findings show that accelerometers, gyroscopes, inertial measurement units, smartphones, smartwatches, and wearable sensors are the dominant data sources for HAR at the edge and a gradual shift from accuracy-centered evaluation toward deployment-aware assessment, incorporating latency, inference time, energy consump...

Adaptive Convolutional Neural Network-Enhanced Scale-Fusion Network for Human Activity Recognition Using Wearable Sensors

A three-stage Adaptive CNN-Enhanced Scale-fusion Network (ACESNet) for six-channel accelerometer–gyroscope activity recognition supports a favorable performance–efficiency trade-off relative to the selected baselines under the stated protocol.

INTELLIGENT RECOGNITION OF HUMAN ACTIVITIES THROUGH MOTION SENSOR PATTERNS FOR CONTEXT-AWARE SYSTEMS

The automatic interpretation of human motion via wearable and mobile sensor technologies has become an important part of the Human Activity Recognition (HAR) topic, which is one of the most important issues in context-aware intelligent systems. This study designed a machine learning-based recognition framework for huma...