Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps

Abstract

The early and objective detection of hand pathologies is a field that still requires more research. One of the main signs of hand osteoarthritis (HOA) is joint degeneration, which causes loss of strength, among other symptoms. HOA is usually diagnosed with imaging and radiography, but the disease is in an advanced stage when HOA is observable by these methods. Some authors suggest that muscle tissue changes seem to occur before joint degeneration. We propose recording muscular activity to look for indicators of these changes that might help in early diagnosis. Muscular activity is often measured using electromyography (EMG), which consists of recording electrical muscle activity. The aim of this study is to study whether different EMG characteristics (zero crossing, wavelength, mean absolute value, muscle activity) via collection of forearm and hand EMG signals are feasible alternatives to the existing methods of detecting HOA patients’ hand function. We used surface EMG to measure the electrical activity of the dominant hand’s forearm muscles with 22 healthy subjects and 20 HOA patients performing maximum force during six representative grasp types (the most commonly used in ADLs). The EMG characteristics were used to identify discriminant functions to detect HOA. The results show that forearm muscles are significantly affected by HOA in EMG terms, with very high success rates (between 93.3% and 100%) in the discriminant analyses, which suggest that EMG can be used as a preliminary step towards confirmation with current HOA diagnostic techniques. Digit flexors during cylindrical grasp, thumb muscles during oblique palmar grasp, and wrist extensors and radial deviators during the intermediate power–precision grasp are good candidates to help detect HOA.

Impact of hand osteoarthritis in women on maximal forces in six different grasp types

“…The proposed method could potentially serve as a non-invasive detection tool, indicating the need for subsequent imaging diagnosis, which is not recommended unless there is a need to rule out other diseases 30 . Finally, during the study, a subset of participants underwent sEMG recording while exerting maximum force using various grasps and sEMG recordings were analysed 31 . The sEMG data yielded valuable information and could be potentially used for differentiation purposes.…”

Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications

“…In the paper “Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps” [ 2 ], researchers explore the potential of electromyography (EMG) in detecting hand osteoarthritis at an early stage. By studying EMG characteristics during hand grasping tasks, the study provides valuable insights into identifying hand osteoarthritis patients before joint degeneration occurs, enabling timely intervention and improved patient outcomes.…”

Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types

“…Finally, there are several sEMG characteristics that can be used to control these devices. Selecting optimal sEMG characteristics, and the best combination between features and channels, are challenging problems for accomplishing satisfactory classification performance [35]- [37]. Improving this classification could, in turn, help to improve the identification of someone's intention when performing a movement or grasp [38].…”