Invasive Device Human Activity Recognition based on Smartphone Dataset

Modeling Human Activity Recognition with Deep Convolutional Neural Networks and Wearable Sensor Data

A deep convolutional neural network (DCNN) based method for recognizing human activities using body-worn sensors’ time-series data after an enormous data analysis on the data.

Understanding Human Motion from Depth Sensors: Activity Recognition and Age Group Recognition Using Skeleton Data

Human Activity Recognition (HAR) plays a significant role in various applications, from learning a discipline to physical rehabilitation. In older adults, activity patterns can indicate levels of frailty, which helps inform the design of physical training programs to prevent falls and maintain mobility. HAR sensing ranges from wearable sensors such as IMUs and RGB cameras to video, specialized gait laboratories, perturbation units, VR, and other modalities. This paper presents a comprehensive study of depth-based, skeleton-driven HAR and age group recognition (AGR) using data collected from real-world nursing home environments. Depth sensors offer a privacy-preserving and non-invasive alternative to wearable and RGB-based systems, enabling continuous 24-h monitoring without requiring user compliance. We systematically evaluate multiple modeling paradigms, including classical machine learning models (DT, RF, KNN, SVM, HMM, HMM+SVM), sequence-based models (LSTM, TCN, ARNN), and graph-based approaches, using skeletal joint data extracted from depth images. Experiments are conducted on two heterogeneous datasets: NTU RGB+D (younger adults) and ETAP-DID (older adults). We analyze the impact of different joint subset configurations (full-body, limb-only, leg-only, and torso-only) and compare raw joint representations with handcrafted time-series features (TSFEL) for frame-based HAR. Beyond activity recognition, we introduce an AGR pipeline to distinguish younger from older adults based on skeletal motion patterns. We investigate multiple feature representations, including absolute joint positions, root-relative coordinates, bone vectors, and joint velocities, and provide interpretability through feature importance and saliency analysis to identify age-discriminative joints and motion cues. Our study provides a comprehensive analysis of various HAR models applied to depth data, examining model performance and the contribution of joint-based features to HAR and AGR. Our study highlights the potential for personalized privacy-preserved monitoring and intervention in nursing homes.

Data Science Approaches to Human Activity Recognition

The main issues that have been identified by the study include sensor noise, user variability and computational constraints and future prospects of the study is given on context-aware systems and edge intelligence.

Advanced Human Activity Recognition Using Wearable Sensors

This study reviews pre-2018 developments and proposes an improved HAR framework incorporating advanced feature engineering, sensor fusion, and ensemble learning techniques, which shows strong potential in healthcare, fitness, and smart environments.

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

Human activity recognition using CNN–BiLSTM with attention on hip-mounted wearable sensors

A deep learning–based HAR framework utilizing hip-mounted accelerometer and gyroscope signals from the USC-HAD dataset, which contains readings from healthy participants only, is evaluated, providing a more realistic assessment of subject-independent generalization across unseen individuals.