Time-series data is widely used across domains but presents challenges due to high dimensionality and noise. This study proposes a hybrid feature selection approach to enhance forecasting accuracy by reducing irrelevant features. We first trained a 1D-CNN-based forecasting model using all features as a baseline. Then, we evaluated four feature selection methods: two traditional (Variance and Dynamic Mode Decomposition) and two XAI-based (SHAP and LIME). To address the limitations of individual methods, we introduced a hybrid approach combining Variance and LIME. Experiments on four diverse datasets, including EEG (seizure forecasting), Pole-balancing (pre-fall detection), Weather (meteorological forecasting), and Electricity (demand prediction), show that the hybrid method consistently outperformed all others. It achieved the highest gains in EEG (+7.34%), Pole-balancing (+9.02%), Weather (+8.62%), and Electricity (+6.72%), demonstrating robust, interpretable, and generalizable performance across tasks.

The lack of real-world data in clinical fields poses a major obstacle in training effective AI models for diagnostic and preventive tools in medicine. Generative AI has shown promise in increasing data volume and enhancing model training, particularly in computer vision and natural language processing (NLP) domains. However, generating physiological time-series data, a common type in medical AI applications, presents unique challenges due to its inherent complexity and variability. This paper introduces TransConv-DDPM, an enhanced generative AI method for biomechanical and physiological time-series data generation. The model employs a denoising diffusion probabilistic model (DDPM) with U-Net, multi-scale convolution modules, and a transformer layer to capture both global and local temporal dependencies. We evaluated TransConv-DDPM on three diverse datasets, generating both long and short-sequence time-series data. Quantitative comparisons against state-of-the-art methods, TimeGAN and Diffusion-TS, using four performance metrics, demonstrated promising results, particularly on the SmartFallMM and EEG datasets, where it effectively captured the more gradual temporal change patterns between data points. Additionally, a utility test on the SmartFallMM dataset revealed that adding synthetic fall data generated by TransConv-DDPM improved predictive model performance, showing a 13.64% improvement in F1-score and a 14.93% increase in overall accuracy compared to the baseline model trained solely on fall data from the SmartFallMM dataset. These findings highlight the potential of TransConv-DDPM to generate high-quality synthetic data for real-world applications.

AI-Powered Edge Computing is accelerating the integration of the cyber world with the ever-growing list of new physical IoT devices and will fundamentally change and empower the way humans interact with the world. In this paper, we prototyped and analyzed three edge computing architectures for running SmartFall, a real-time fall detection application that uses accelerometer data from the watch, to compare the trade-off in relationship to battery consumption, potential data loss, machine learning model's prediction accuracy, and latency in model inferencing. Our experiments show that running the machine learning prediction on the server using the TensorFlow native model format has achieved the best model accuracy with-out draining the battery power of the smartwatches. However, the optimal selection of the software architecture depends on the intended deployment environment, projected user numbers, users' privacy concerns, and network stability.