Gait Monitoring and Analysis: A Mathematical Approach

Abstract

Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson’s, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson’s disease by monitoring their gait due to wearable devices that can accurately detect a person’s movements. In our study, about 50 people were involved in the trial (20 with Parkinson’s disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the “gait quality” based on the measure of entropy obtained by applying the Fourier transform.

Multifunctional, High-Strength Electronic Skin Based on the Natural Sheepskin Fiber Network for Multifaceted Human Health Monitoring and Management

“…Foot condition detection is a technology that detects and analyzes the condition of the human foot. It is mainly used to evaluate foot health, prevent, and assist in the treatment of foot injuries or diseases. − In past examples of using flexible sensing materials for detection, most of the health detection was done only around joint movement, while ignoring the foot area closely related to human activities, which was undoubtedly not comprehensive. − …”