Human Activity Recognition presents a multifaceted challenge, encompassing the complexity of human activities, the diversity of sensors used, and the imperative to safeguard user data privacy. Recent advancements in machine learning, deep learning, and sensor technology have opened up new possibilities for human activity recognition. Wearable sensor-based human activity recognition involves collecting time-series data from various sensors, capturing intricate aspects of human activities. The focus of the above activity recognition problem is classifying human activities from the time-series data. Hence, this time-series classification problem demands efficient utilization of temporal properties. Moreover, while accurate prediction is crucial in human activity recognition, the reliability of predictions often goes unnoticed. Ensuring that predictions are reliable involves addressing two issues: calibrating miscalibrated predictions that fail to accurately represent the true likelihood of the data and addressing the challenges around uncertain predictions. Modern deep learning models, used extensively in human activity recognition, often struggle with the above issues. In addition to reliability concerns, machine learning algorithms employed in Human Activity Recognition are also plagued by privacy issues stemming from the utilization of sensitive activity data during model training. While existing techniques such as federated learning can provide some degree of privacy protection in these scenarios, they tend to adhere to a uniform concept of privacy and lack quantifiable privacy metrics that can be effectively conveyed to users and customized to cater to their individual privacy preferences. Hence, in the thesis, we identify the challenges around the effective use of temporal data, reliability, and privacy issues of machine learning models used for wearable sensor-based human activity recognition. To tackle these challenges, we put forth novel solutions, striving to enhance the overall performance and trustworthiness of machine learning models employed in human activity recognition.

Firstly, to improve classification performance, we propose a new temporal ensembling framework that uses data temporality effectively. The framework accommodates various window sizes for time-series data and trains an ensemble of deep-learning models based on that. It enhances classification accuracy and preserves temporal information.

Secondly, we address reliability through calibration and uncertainty estimation. The aforementioned temporal ensembling framework is used for calibration and uncertainty estimation. It provides well-calibrated predictions for human activity recognition and detects out-of-distribution activities, an important task of uncertainty estimation. Furthermore, we apply these methods to real-world scenarios, enhancing the reliability of human activity recognition models.

Thirdly, to address the privacy concern, we introduce a differentially private framework for time-series human activity recognition, quantifying privacy. Additionally, we develop a collaborative federated learning framework, allowing users to define their privacy preferences, advancing privacy preservation in human activity recognition.

These contributions address major challenges and promote improved classification, reliability, and privacy preservation in human activity recognition. It helps us to move towards trustworthy machine learning in human activity recognition, facilitating their usage in realistic and practical scenarios.