The early detection of pressure ulcers, an optimized movement monitoring through machine learning and wearable sensor technology

Abstract: This study investigates how machine-assisted motion analysis can contribute to the prevention of pressure ulcers in bedridden patients.Pressure ulcers, also known as bedsores, develop due to prolonged pressure on the skin and underlying tissues, which impairs blood circulation. Without sufficient movement and regular repositioning, affected areas may no longer receive adequate oxygen and nutrients, ultimately leading to tissue damage and open wounds. Immobile patients are particularly at risk, as their prolonged inactivity significantly increases the likelihood of developing pressure ulcers. The early detection of critical movement patterns is therefore essential to initiate preventive measures such as repositioning or targeted positioning strategies in a timely manner. The objective of this study is to define a movement threshold using machine learning algorithms that distinguishes between insufficient, adequate, and excessive movement. Both continuous and interval-based classification methods are employed to identify patterns associated with an increased risk of pressure ulcers. Additionally, skin temperature variations are incorporated into the analysis, as they may indicate reduced blood circulation—an important early warning sign for pressure ulcer formation. To capture movement data, the Pixel Watch 3 is used as a wearable sensor technology. The smartwatch is attached to the patient's body to enable a more precise detection of movement patterns. Various positions are tested to determine which placement provides the most reliable sensor data regarding patient mobility and positional changes. This analysis aims to identify the optimal smartwatch placement for achieving the most accurate classification results. Several sensors are utilized, including an accelerometer to measure movement intensity, a gyroscope to detect rotations, a posture sensor to track positional changes, a skin temperature sensor to assess blood circulation variations, and a heart rate sensor to capture physiological responses. The smartwatch is tested in three positions: chest, abdomen, and ankle. A total of 10 participants are included in the study. Five micro-movements have been identified, and for each of these movements, 20 labels are generated per participant, each lasting 10 seconds. The data collection is conducted at a sampling rate of 20 Hz. This results in a total of 14 (sensors) * 10 (participants) * 20 Hz (sampling rate) * 10 seconds (label duration) * 20 labels (number of labels) * 5 movements (micro-movements) * 3 positions (chest, abdomen, ankle) = 8,400,000 data records.The collected movement data is used to develop a machine-learning model that detects movement deficiencies at an early stage and automatically alerts caregivers before critical situations arise. The model distinguishes between sufficient, insufficient, and excessive movement, enabling targeted interventions. The study results demonstrate that the Pixel Watch 3 can be utilized as a precise monitoring tool, allowing for continuous movement tracking. Such a system could significantly contribute to reducing the workload of healthcare professionals by facilitating targeted interventions while simultaneously improving the quality of patient care. This study provides a vital foundation for the future development of intelligent care systems that not only optimize pressure ulcer prevention but also enhance the efficiency and accuracy of nursing documentation through wearable sensor technologies and machine learning. Furthermore, the system is tested in an experimental setting to evaluate its practicality and effectiveness. The goal is to obtain a realistic assessment of how well the model can be integrated into real-world nursing practice and to determine what adjustments are necessary for its optimal implementation within existing care structures.

Keywords: Human Motion Analysis, Machine Learning, Health Informatics, Activity Recognition

DOI: 10.54941/ahfe1006720

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