## 2 Summary of the Contributions  
Paper [1] addresses the challenge of recognizing physical activity in individuals with spinal cord injuries (SCI) using sensor-based approaches. SCI patients often experience health complications like obesity and muscle weakness, necessitating effective rehabilitation. Existing methods, relying on patient surveys, may not accurately capture actual activity levels. The advent of physical activity recognition systems presents a more reliable solution. The paper compares vision-based and sensor-based approaches, favoring wearable sensors for their affordability and ease of use. Sensor placement is crucial, with lower-limb sensors suited for locomotion and upper-limb activity requiring wrist and upper-arm sensors. This paper explores various applications of activity recognition, particularly in rehabilitation monitoring. Segmenting continuous raw data before classification is a key challenge. While the sliding window approach is common, selecting an appropriate window size is vital to ensuring accuracy. Short windows may truncate activities, while long windows can merge them. Adaptive window techniques have been proposed to address this issue. The paper introduces a novel segmentation method tailored to dynamic activities in rehabilitation. Anexperiment compares this approach to the fixed sliding window method, demonstrating its effectiveness in boosting recognition accuracy. This study, which employs wrist-worn accelerometers, reveals an accuracy improvement of over 5%, enhancing model robustness and successfully classifying similar activities. The method achieves recognition rates exceeding 91% with various ML classifiers, particularly the support vector machine. Wearable sensors, such as accelerometers, prove invaluable in rehabilitation assessment. The study acknowledges the potential limitations, while also highlighting the method's potential for segmenting and recognizing physical activities, especially in rehabilitation scenarios. In [2], a framework for analyzing consumer behavior in the rapidly growing over-the-top (OTT) media consumption market was introduced, considering factors such as pricing, service delivery, and infrastructure investments. This paper addresses the challenge of an imbalanced consumer distribution within the OTT market, and the need for accurate analysis reflecting changing market conditions due to factors like the COVID-19 pandemic. This paper highlights the rapid growth of the OTT market, driven by the availability of digital media content via the internet and IP-based paths. Platforms like YouTube, Netflix, and Amazon Prime are transforming the media landscape, threatening traditional markets. With a projected market value of over a trillion dollars by 2027, various service providers and telecom carriers are competing fiercely in this domain. The COVID-19 pandemic has further accelerated the growth of OTT consumption, making it vital to analyze consumer behavior effectively. The proposed framework combines a conditional probability-based approach with machine learning techniques, such as support vector machines, k-nearest neighbors, and decision trees. This approach enhances classification performance, particularly for imbalanced consumer groups. The framework also adapts to changing consumer trends by dynamically retraining with incoming OTT consumer data. It yields improved classification accuracy, particularly for lower-number classes, showing a recall-based improvement of 5.3% to 19.2%. Unlike conventional methods, the proposed framework maintains consistently high performance, even as the OTT market environment changes. The study underscores the practical significance of the framework for companies participating in the OTT market, offering a stable performance in a dynamic environment. Paper [3] investigates demand forecasting for the automotive original equipment manufacturer (OEM) sector, assessing 21 baseline, statistical, and ML algorithms.