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.

Remote Monitoring Model for the Preoperative Prehabilitation Program of Patients Requiring Abdominal Surgery

“…Unsupervised programs tend to be restricted to modes of exercise that are most readily available in the community and include walking, running, and cycling [8,9]. Different modes of exercise can be identified by using Fast Fourier Transformation (FFT) amplitude and frequency measures of data attained from 3D accelerometers during the activity [12] (Table 1). For example, cycling produces frequency and amplitude values that differ from rowing.…”

“…Different activities at a particular intensity will produce specific amplitude and frequency measures. For example, for older people, walking at a low intensity (3.5 kph) will produce lower frequency and amplitude measures than walking at a high intensity (4.5 kph) [12].…”

“…Furthermore, sample rates of accelerometers must be high enough to detect the repetitive short burst of high intensity activity followed by low activity or rest that occurs during HIIT. Our previous research showed that 3D accelerometer FFT analysis had the potential to accurately identify type and intensity of exercise over relatively short periods of time [12]. The volume of exercise provides the clinician with an indication of total amount (volume) of exercise or energy expenditure and is a product of the intensity, duration, and frequency.…”

“…As explained in previous sections, a specific physical activity and corresponding intensity can be detected from dominant frequency and amplitude values calculated from accelerometer data. Data relevant to the nine activities of interest are published in our previous work [12] and are shown in Table 3.…”

“…This paper builds on our previous work [12], which developed a 3D accelerometer sensor system that recognized different types and intensities of physical activities. It attempts to formulate a knowledge structure on the physical activities typically prescribed to abdominal cancer patients during the prehabilitation period.…”