KNEE-HAR: A ConvLSTM-Based Lightweight Hybrid Deep-Learning Model for Human Activity Recognition With Knee Abnormalities Using Raw Physiological Sensor Data

Human activity recognition (HAR) is one of the most exploited areas in the field of artificial intelligence and wearable sensor technology. Most of the work on HAR classifies simple to complex human activities but does not manage to recognize the actions of an individual with distinct physical abnormalities. This article proposes an efficient and lightweight hybrid deep learning model (ConvLSTM) to classify human activities with knee abnormalities. A state-of-the-art publicly available physiological sensor-based HAR dataset with knee abnormalities is incorporated to validate the proposed framework. A minimal data preprocessing pipeline was also introduced to prepare the dataset for categorization without interfering with its raw characteristics. Further, optimal hyperparameters with an efficient time-step-based data segmentation method were developed for automatic feature engineering in spatial and temporal frequency domains. Moreover, incorporating regularization and low-cost mathematical functions resulted in a sparse and prominent feature space, improving performance by overcoming overfitting and optimizing computational times. The proposed model achieved the highest accuracy of 99.21% and an average accuracy of 99% in comparatively optimized computational time than the benchmark and state-of-the-art models.