Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment

Sensors (Basel). 2024 Nov 16;24(22):7330. doi: 10.3390/s24227330.

Abstract

Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes and further medical problems. Recent technological and artificial intelligent advancements have equipped healthcare professionals with innovative tools, such as sensor systems and telemedicine equipment, offering groundbreaking opportunities to monitor and analyze patients' physical activity. This paper investigates the potential applications of mobile accelerometers in evaluating the symmetry of specific rehabilitation exercises using a dataset of 1280 tests on 16 individuals in the age range between 8 and 75 years. A comprehensive computational methodology is introduced, incorporating traditional digital signal processing, feature extraction in both time and transform domains, and advanced classification techniques. The study employs a range of machine learning methods, including support vector machines, Bayesian analysis, and neural networks, to evaluate the balance of various physical activities. The proposed approach achieved a high classification accuracy of 90.6% in distinguishing between left- and right-side motion patterns by employing features from both the time and frequency domains using a two-layer neural network. These findings demonstrate promising applications of precise monitoring of rehabilitation exercises to increase the probability of successful surgical recovery, highlighting the potential to significantly enhance patient care and treatment outcomes.

Keywords: abdominal wall repair; accelerometers; computational intelligence; machine learning; motion symmetry; physical activity monitoring; rehabilitation.

MeSH terms

  • Accelerometry* / methods
  • Adolescent
  • Adult
  • Aged
  • Bayes Theorem
  • Child
  • Exercise / physiology
  • Female
  • Humans
  • Machine Learning
  • Male
  • Middle Aged
  • Mobile Applications
  • Neural Networks, Computer*
  • Signal Processing, Computer-Assisted
  • Support Vector Machine
  • Young Adult

Grants and funding

This investigation was reinforced by the European Union under the project ROBOPROX—Robotics and Advanced Industrial Production (reg.no. CZ.02.01.01/00/22_008/0004590) in the area of machine learning. The research related to data acquisition and their computational processing was supported by Operational Programme Johannes Amos Comenius financed by European Structural and Investment Funds and the Czech Ministry of Education, Youth and Sports (Project No. SENDISO—CZ.02.01.01/00/22_008/0004596).