ACM Transactions on Embedded Computing Systems (TECS)
Creating and benchmarking a new dataset for physical activity monitoring
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Towards robust activity recognition for everyday life: methods and evaluation
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
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Chronic obstructive pulmonary disease (COPD) is a major public health problem. Early detection and treatment of an exacerbation in the outpatient setting are important to prevent worsening of clinical status and need for emergency room care or hospital admission. In this study we use accelerometers to capture motion data; and heart rate and respiration rate to capture physiological responses from patients with COPD as they perform a range of Activities of Daily Living (ADL) and physical exercises. We present a comparative analysis of classification performance of a set of different classification techniques and factors that affect classification performance for activity recognition based on accelerometer data. This is the first step towards building a wearable sensor monitoring system for tracking changes in physiological responses of patients with COPD with respect to their physical activity level.