Tri-Axial Accelerometer-Based Recognition of Daily Activities Causing Shortness of Breath in COPD Patients

Abstract

Background: The 6 min walk test is widely used to measure the severity of COPD, but this test imposes an extensive burden on patients and medical staff. As an alternative to this test, the measurement of activity amount during daily activities that cause COPD patients to feel shortness of breath can be acquired using wearable devices, and the measurements between COPD patients and healthy people can be compared. Accordingly, based on machine learning, we first evaluated the accuracy of the accelerometers in recognizing such activities. Methods: Forty-six healthy participants wore tri-axial accelerometers on the wrist and hip, and performed nine activities: changing clothes, sitting, standing, lying in a supine position, brushing teeth, moving luggage, going up/down the stairs, running, and walking. Features were extracted from 10 s windows of 31 datasets and input into a machine-learning classifier model. Results: The wrist+hip classifier recognized the activities of changing clothes, standing, lying in a supine position, brushing teeth, moving luggage, going up/down the stairs, walking, and running. Sitting and other movements were not appropriately recognized. Conclusions: Sedentary movements such as sitting were difficult to recognize. However, changing clothes, standing, lying in a supine position, brushing teeth, moving luggage, going up/down the stairs, walking, and running were recognizable. These recognized activities tend to cause shortness of breath in COPD patients. Thus, the findings confirm the feasibility of an algorithm that could determine the severity of COPD by comparing activity amount for each of these activities with those of the healthy people of the same age.

Keywords

• chronic obstructive pulmonary disease
• wearable electronic devices
• activity trackers
• machine learning
• artificial intelligence
• classification