TY  - EJOU
AU  - Alhayan, Fatimah 
AU  - Hanzla, Muhammad 
AU  - Alsolai, Hadeel 
AU  - Alabdullah, Bayan 
AU  - Jalal, Ahmad 
AU  - Liu, Hui 

TI  - Attention-Driven Multi-Kernel Edge–Cloud Learning for Wearable IMU-Based Human Activity Recognition in IoT Health Monitoring
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Human activity recognition (HAR) from wearable inertial sensors underpins IoT-based smart-health monitoring, yet practical deployments are constrained by noisy Inertial Measurement Unit (IMU) signals, inter-subject variability, temporal misalignment across sensor streams, and the limited compute available on edge nodes. Existing pipelines typically optimize recognition accuracy in isolation from these deployment constraints. This paper addresses both aspects through a distributed edge–cloud HAR framework. At the edge, IMU signals are denoised using Tukey filtering and segmented using Planck-Taper windowing before compact features are transmitted, reducing uplink overhead. At the cloud, an attention-based deep multiple-kernel learning (A-DMKL) adaptively combines multi-resolution representations (Stockwell Transform (S-Transform), Gramian Angular Summation Field (GASF), Synchrosqueezed Wavelet Transform (SSWT), Hankel), which are optimized using the Coyote Optimization Algorithm (COA) and classified through Soft Dynamic Time Warping Soft-DTW temporal alignment, a Hierarchical Hidden Semi-Markov Model, and an InceptionTime ensemble. Under strictly subject-independent leave-one-subject-out evaluation on 24 and 8 participants, with all fitting steps confined to the training folds, the framework attains 87.22% and 91.33% accuracy on the Hang-Time HAR and VIMPT2019 datasets, respectively. To assess deployment robustness, the proposed pipeline is analyzed under simulated network service profiles, Message Queuing Telemetry Transport (MQTT) packet loss, and inter-stream synchronization error, with all simulation assumptions explicitly stated. The framework targets communication-efficient human activity recognition for IoT healthcare applications. Clinical validation and deployment on Advanced RISC Machine (ARM-class) embedded hardware remains beyond the scope of the present study and are identified as future work.
KW  - Human activity recognition; wearable inertial sensors; multiple kernel learning; attention mechanism; time–frequency analysis; edge–cloud computing; Internet of Things (IoT); smart health monitoring

DO  - 10.32604/cmc.2026.086339