LITEWAY: LIghtweight HAR via Temporal Efficient highWAY
2026-08-10 • Machine Learning
Machine LearningArtificial IntelligenceHuman-Computer Interaction
AI summaryⓘ
The authors address challenges in wearable human activity recognition (HAR) caused by limited computing power and battery life on small devices. They developed LITEWAY, a new method using only convolutional layers instead of slower recurrent ones to analyze sensor data more efficiently. Their approach reduces model size and energy consumption significantly while maintaining good accuracy across many datasets. This makes it easier to run HAR on devices like smartwatches with less lag and power use.
Human Activity RecognitionWearable SensorsConvolutional Neural NetworksRecurrent Neural NetworksGRULSTMModel CompressionEnergy EfficiencyTime Series AnalysisDeep Learning
Authors
Dominique Nshimyimana, Vitor Fortes Rey, Mengxi Liu, Bo Zhou, Paul Lukowicz
Abstract
Wearable human activity recognition (HAR) remains challenging due to the computational and energy constraints of deep learning models on resource-limited devices. Existing lightweight approaches often rely on recurrent architectures (e.g., GRU and LSTM), limiting parallelism and increasing inference latency. We propose LITEWAY, a modality-agnostic, fully convolutional framework for multichannel sensor time series that replaces recurrent temporal modeling with structured convolutional decomposition. LITEWAY combines lightweight convolutional blocks, strided temporal processing, and convolution-attention pooling to efficiently capture temporal dependencies while reducing computational complexity. We evaluate LITEWAY on 16 HAR datasets against TinyHAR, TinierHAR, and MLP-HAR. LITEWAY achieves competitive macro F1 while reducing model size by 4.06x-9.52x (Light) and 3.87x-9.07x (Full) compared with TinyHAR and TinierHAR. Deployment experiments further show energy reductions of 2.29x-3.14x (Light) and 1.46x-2.01x (Full) compared with TinierHAR and MLP-HAR, highlighting efficient fully convolutional temporal modeling for wearable HAR. The source code is publicly available at https://github.com/dominique-nshimyimana/liteway.