Human Movement Monitoring and Analysis for Prehabilitation Process Management

Abstract

Cancer patients assigned for abdominal surgery are often given exercise programmes (prehabilitation) prior to surgery, which aim to improve fitness in order to reduce pre-operative risk. However, only a small proportion of patients are able to partake in supervised hospital-based prehabilitation because of inaccessibility and a lack of resources, which often makes it difficult for health professionals to accurately monitor and provide feedback on exercise and activity levels. The development of a simple tool to detect the type and intensity of physical activity undertaken outside the hospital setting would be beneficial to both patients and clinicians. This paper aims to describe the key exercises of a prehabilitation programme and to determine whether the types and intensity of various prehabilitation exercises could be accurately identified using Fourier analysis of 3D accelerometer sensor data. A wearable sensor with an inbuilt 3D accelerometer was placed on both the ankle and wrist of five volunteer participants during nine prehabilitation exercises which were performed at low to high intensity. Here, the 3D accelerometer data are analysed using fast Fourier analysis, where the dominant frequency and amplitude components are extracted for each activity performed at low, moderate, and high intensity. The findings indicate that the 3D accelerometer located at the ankle is suitable for detecting activities such as cycling and rowing at low, moderate, and high exercise intensities. However, there is some overlap in the frequency and acceleration amplitude components for overland and treadmill walking at a moderate intensity.

Keywords

• prehabilitation
• movement activity recognition
• sensors in healthcare applications
• 3d accelerometer