Empirical Mode Decomposition-Derived Entropy Features Are Beneficial to Distinguish Elderly People with a Falling History on a Force Plate Signal

Abstract

Fall risk prediction is an important issue for the elderly. A center of pressure signal, derived from a force plate, is useful for the estimation of body calibration. However, it is still difficult to distinguish elderly people’s fall history by using a force plate signal. In this study, older adults with and without a history of falls were recruited to stand still for 60 s on a force plate. Forces in the x, y and z directions (Fx, Fy, and Fz) and center of pressure in the anteroposterior (COPx) and mediolateral directions (COPy) were derived. There were 49 subjects in the non-fall group, with an average age of 71.67 (standard derivation: 6.56). There were also 27 subjects in the fall group, with an average age of 70.66 (standard derivation: 6.38). Five signal series—forces in x, y, z (Fx, Fy, Fz), COPX, and COPy directions—were used. These five signals were further decomposed with empirical mode decomposition (EMD) with seven intrinsic mode functions. Time domain features (mean, standard derivation and coefficient of variations) and entropy features (approximate entropy and sample entropy) of the original signals and EMD-derived signals were extracted. Results showed that features extracted from the raw COP data did not differ significantly between the fall and non-fall groups. There were 10 features extracted using EMD, with significant differences observed among fall and non-fall groups. These included four features from COPx and two features from COPy, Fx and Fz.

Trend Change Analysis as a New Tool to Complement the Evaluation of Human Body Balance in the Time and Frequency Domains

“…Among the most frequently analyzed time measures are length of the support path (Path), COP movement velocity, the surface area of the ellipse of the load (Area), load symmetry, and range of COP motion (Range of Motion) ( Błaszczyk, 2008 ; Jurkojć, 2018 ; Takakusaki, 2017 ). In addition to these measurements, entropy and frequency values can be calculated to supplement the data ( Błażkiewicz et al, 2021 ; Chou et al, 2021 ). The latter method finds application in the investigation of factors disturbing the balance of cyclic characters, both in the form of a movable ground, and the oscillation of virtual surroundings in a virtual reality environment.…”

Analysis of Center of Pressure Signals by Using Decision Tree and Empirical Mode Decomposition to Predict Falls among Older Adults

“…Chang further investigated the topic by analyzing COP signals using empirical mode decomposition (EMD) and extracting intrinsic mode functions (IMFs) to compute the entropy features. Chang discovered significant differences between nonfallers and fallers in multiple parameters [ 11 ].…”

Testing the efficacy of a motor analogy designed to promote safe landing by older adults who fall accidentally: a study protocol for a randomised control study

“…Accidental falls can adversely affect the health of older people and are second only to traffic incidents as the most common cause of death 1. Millions of older adults fall each year.…”

“…Accidental falls can adversely affect the health of older people and are second only to traffic incidents as the most common cause of death. 1 Millions of older adults fall each year. Not only are falls associated with high personal costs, such as reduced well-being, but also healthcare sectors are heavily burdened.…”