Sensors worn on the body enable unobtrusive recording of physical activities, sleep, and stress, supporting the quantified self trend. Utilizing MEMS (Micro-Electro-Mechanical Systems), numerous electronic devices can enhance daily activities, appealing to both private users and industrial applications for continuous monitoring. Human activity recognition involves sensor-based, continuous tracking of physical activities, requiring interdisciplinary research that intersects with medicine, psychology, and industrial science. Given that sensors provide a simplified view of reality, data analysis and contextualization are essential. Innovative solutions are necessary for sensor technology, data processing, machine learning, and new human-machine interfaces tailored to specific applications. The unobtrusive monitoring enabled by small electronic systems attached to the body offers complex support across medicine, work, and leisure. Advances in miniaturization and algorithms expand the potential for activity recognition, facilitating assistance anytime and anywhere. This evolution necessitates a rethinking of personal assistance and human-machine interactions. The first workshop on sensor-based activity recognition, WOAR 2014, gathered scientists and users to share experiences and present findings. Discussions focused on technologies for recognizing physical activity using inertial sensor systems and their practical applications. Th
