WiFi sensing enables non-intrusive, device-free monitoring of human activities by analyzing Channel State Information (CSI) extracted from commodity WiFi signals. While most research has studied Human Activity Recognition on pre-segmented clips, the harder problem of temporal activity segmentation — partitioning a continuous CSI stream into labeled activity intervals — has received less attention, and progress is limited by the absence of high-quality datasets and standardized evaluation infrastructure.
This thesis addresses that gap through three interconnected contributions. First, we introduce WiPos, a multimodal dataset in which a subject performs activities at freely varying positions, annotated with millisecond-scale precision using motion capture. Second, we present Breaking-CSI, a unified benchmarking framework that enables fair, reproducible comparison of segmentation methods across multiple datasets. Third, we propose DopplerTAS, a temporal activity segmentation model that operates on Doppler features derived from the time-differential CSI phase rather than raw amplitude, making predictions largely position-invariant.
Experiments using Breaking-CSI to evaluate representative baselines from the literature show that all of them suffer a consistent accuracy drop on WiPos compared to their native datasets, confirming that positional variation is the dominant challenge. DopplerTAS achieves 96.7% frame accuracy and 90.4% mIoU on WiPos, improving over 30 percentage points on both metrics.
Together, these contributions provide the dataset quality, evaluation thoroughness, and modeling approach needed to advance WiFi-based temporal activity segmentation from isolated recognition experiments toward continuous, position-robust sensing.

This report details the design and implementation of a subsystem providing a web-based platform for smartwatch data acquisition. A smartwatch application is developed to read out the accelerometer, gyroscope and heart rate sensor on the smartwatch and transmit the sensor data to the platform. A platform is developed to receive the data and store it in a database. Recordings can be downloaded from the platform to use in research of human activity. The platform presents the type of activity the smartwatch user is doing using machine learning models, by integrating the other subsystem. Multiple tests have been performed to analyse and improve the performance of the system. The battery life of the smartwatch has been tested using various settings in the smartwatch application to determine the most power-efficient settings.