# Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor
## 1 Introduction  
Individuals with spinal cord injuries (SCI) who rely on wheelchairs typically experience associated symptoms such as obesity and low muscular strength. These symptoms may eventually lead to secondary complications, including diabetes and cardiovascular diseases [1, 2]. Rehabilitation processes, such as in-home strength exercises, play an essential role in avoiding such symptoms and redeveloping the motor skills that are needed to perform daily activities and promote quality of life [3, 4]. Currently, therapists rely on patient surveys to measure their adherence to these activities. However, studies indicate wide variability between self-reported and actually performed physical activity, which can undermine rehabilitation progress [5]. Nevertheless, with rapid technological innovation, physical activity recognition systems are emerging as a more reliable way to detect these activities [6, 7, 8, 9]. Based on the approach used to collect data, activity recognition can be broadly classified into two approaches: the vision-based and sensor-based approaches. Although the vision-based approach is information-rich, it often suffers from ethical and privacy concerns, especially in healthcare applications when dealing with patients. By contrast, the devices used in the sensor-based approach, including wearable sensors, can operate with limited cost and power, and they have no restrictions in terms of the surroundingenvironment or the location where activities must be performed. As a result, activity recognition systems commonly adopt the sensor-based approach [10]. Several studies have been undertaken to investigate the impact of different sensor positions on overall recognition accuracy. These studies indicate that sensor position should be determined mainly based on the type of activity under study. Forms of locomotion, including walking and running, as well as static activities, such as standing and sitting, can be recognized with an accuracy of between 83% to 95% using lower-limb segments (hip, thigh, and ankle) as the sensor positions. To improve accuracy when recognizing upper-limb activities, sensors are placed on the wrist and upper arm [11]. Within this context, the study in [12] considered different positions, such as hip, belt, wrist, upper arm, ankle, and thigh, to recognize 20 types of activities, including both upper- and lower-limb activities. The results showed high accuracy when combining different positions. However, the study also demonstrated a slight performance decrease when using only the thighs and wrists. In addition to the impact of sensor placement on accuracy, user preferences should be considered to gain acceptance. To address this problem in the design of wearables, a meta-analysis was undertaken in [13]. The study concluded that people prefer wearing sensors on their wrist, followed by the trunk, belt, ankle, and, finally, armpit. Activity recognition systems have a wide variety of applications, including rehabilitation and physical therapy. These systems allow monitoring of patients and the identification of exercises being performed [14]. In this regard, [PERSON] et al. [15] proposed a monitoring system consisting of a network of wearable accelerometers and a smartphone to recognize the intensity of specific physical activities (e.g., strength exercises). The system used two Support Vector Machine (SVM) layers to detect the type of activity being performed and determine its intensity. The study demonstrated that the hierarchical algorithm achieved an accuracy of approximately 85% in recognizing a set of upper-body activities. The study in [16] presented a methodology to recognize three fundamental arm movements using two different classifiers: Linear Discriminant Analysis (LDA) and SVM. The overall average accuracy was 88% using data collected from accelerometers and 83% using gyroscope data. With the same objective, [PERSON] et al. [10] designed a model to recognize three physical activities of the human forearm, relying on data collected from a single wrist-worn accelerometer.