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.
Abstract
Human activity recognition (HAR) is the identification of daily human activities using wearable sensor data. In this study, we evaluate 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. The proposed pipeline integrates convolutional feature extraction, bidirectional long short-term memory modeling, and an additive attention mechanism to capture temporal dependencies in the sensor data. The model is evaluated using performance matrices and leave-one-subject-out cross-validation (LOSO-CV) to assess subject-independent generalization. Performance is reported using accuracy, precision, recall, F1-score, and 95% confidence intervals, and statistical significance testing. Our experimental results show that under subject-exclusive splitting, the proposed model achieves 98% accuracy. Under strict LOSO-CV, the model achieves a performance of 78% ± 0.1130, providing a more realistic assessment of subject-independent generalization across unseen individuals. The dataset does not include clinical or patient populations. The findings are limited to non-clinical settings and should be interpreted within this scope. The results primarily contribute methodological insights into wearable-based HAR systems. The potential of this work for healthcare applications is discussed as a direction for future research, subject to validation on clinically representative datasets.
Wearable sensor-based human activity recognition (HAR) has become increasingly popular for applications in health monitoring, fitness, and smart living. But the use of deep learning models on edge devices is still challenging due to limited memory and computational power. In this paper, we develop a resource-constrained CNN-GRU hybrid model for HAR on the WISDM dataset. This architecture uses convolutional layers for spatial learning and gated recurrent units (GRU) for sequence learning. For deployment on the edge, the model is quantized to TensorFlow Lite (TFLite) using float16. Our experiments show that the model achieves an accuracy of 94.07%, while the size of the model is substantially smaller and suitable for deployment on edge devices. Importantly, the TFLite model maintains the same accuracy as the original model, ensuring its suitability for real-time deployment. The extensive assessment through confusion matrices, ROC curves and classification metrics confirms the effectiveness of the model across various activities. The proposed approach offers a balance between accuracy and computational efficiency, enabling real-time HAR on edge devices.
Unknown authors· International journal of com...· 0 citations
Human Activity Recognition (HAR) from smartphone accelerometer data is widely studied on the WISDM dataset, but random sample-based partitions can allow windows from the same subject to appear in both training and evaluation data, producing optimistic estimates of cross-subject generalization. This paper investigates the DeepConvLSTM architecture on WISDM v1.1 using only tri-axial smartphone accelerometer signals under a subject-disjoint split comprising 25 training subjects and an 11-subject evaluation partition. Nine controlled experiments varied window configuration, model capacity, temporal pooling, and augmentation strategy. Under this single fixed subject split, without repeated random seeds or statistical comparisons, the best-performing configuration among the nine experiments used a 60-sample window (3 s) with 50% overlap and on-the-fly jitter and scaling augmentation, achieving 90.12% accuracy, compared with 89.12% for offline augmentation, 88.75% for class-specific augmentation, and 87.49% with label smoothing. Global Average Pooling showed an observed class-level trade-off relative to last-timestep pooling in the evaluated comparison rather than a general architectural advantage. A persistent 8–10 percentage-point training–evaluation gap remained, with notable confusion among stair-related locomotion classes, which may partly reflect limited subject diversity, class imbalance, and accelerometer-only sensing. Importantly, the same 11-subject evaluation partition was consulted for early stopping, learning-rate scheduling, comparison of all nine experiments, and final model selection; therefore, the reported 90.12% accuracy should not be interpreted as performance on a fully untouched test set. These findings provide a leakage-resistant but selection-sensitive benchmark for subject-independent DeepConvLSTM evaluation on WISDM.
Fakhrul Zidan Nurrohman, C. Dewa· bit-Tech· 0 citations
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.
S. Islam, Kamrul Hasan Talukder· International Journal of Int...· 0 citations
Accurate detection of sedentary behavior is important for studying health risks related to prolonged sitting, but posture-based classification remains challenging with wearable sensors, especially at the wrist. We study whether a deep learning model trained on hip-worn accelerometer data can transfer to wrist-worn accelerometer data for sitting versus non-sitting classification. We use CHAP, a CNN-BiLSTM model originally developed for hip accelerometers, and evaluate its zero-shot performance on wrist data as well as its adaptation through finetuning with varying amounts of labeled wrist data. Experiments are conducted on the iWatch dataset with ground-truth posture labels derived from wearable cameras. The hip-trained model performs strongly on hip data without retraining, but accuracy drops on wrist data due to sensor placement shift. Finetuning CHAP provides consistent advantages over transformer models trained from scratch. These findings suggest that hip-based pretraining provides a useful starting point for wrist deployment, while highlighting the need for wrist-specific adaptation to handle higher signal variability.
Yuliang Chen, Weiwei Shi, Jingjing Zou et al.· 0 citations
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.
Zainab Abdullahi· International Journal of App...· 0 citations
Wearable-based human activity recognition (HAR) has emerged as a valuable method for capturing activities across diverse domains, including rehabilitation, occupational ergonomics, sports, and human-computer interaction (HCI). While recognition performance can be significantly enhanced by leveraging multiple complementary sensors, this approach requires accurately synchronized time bases across all devices. Although previous studies on synchronization in HAR suggested that sub-second accuracy is advisable while sub-100 ms accuracy is unnecessary, the specific effect of time discrepancies on machine learning models has, so far, remained unexplored. We address this gap by introducing an experimental paradigm for systematically evaluating the impact of time discrepancies in multi-wearable HAR, which we evaluated in two experiments. In our first experiment, we use the example of multi-stage temporal convolutional networks (MS-TCN) for sequence-to-sequence action segmentation, simulating the time discrepancies of time offset and clock skew via rational resampling. Our evaluation spanned 30,025 training and validation runs across different model configurations, totaling over one million core-hours of computation. Our results reveal that time offsets larger than 167 ms should be avoided in training datasets, and offsets beyond 333 ms can already significantly degrade HAR performance for typical activities of daily living (ADLs). Subsequently, we performed a second experiment focusing on the impact of time offsets on inference in models trained on synchronized datasets. Our evaluation spanned temporal convolutional networks, LSTMs, and Transformer architectures across five architectural configurations, each with two different temporal input lengths. The results indicate that LSTMs for action segmentation are more robust to desynchronization, while other architectures exhibited a marked performance degradation beyond desynchronization offsets spanning 167 ms. Our findings have implications for the design and deployment of multi-wearable HAR systems and may extend to other multi-sensor contexts.
David Kostolani, Florian Wolling, S. Schlund et al.· Frontiers of Computer Scienc...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
