Gaussian mixture model based clustering of Manual muscle testing grades using surface Electromyogram signals

Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions

This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT-UMAP combination stands out in the evaluation metrics.

Influence of hand dominance, gender, and body mass index on hand grip strength

Background

Hand grip strength (HGS) measurements serve as an objective measure of upper extremity function. Reliable hand strength evaluation is vital for assessing treatment effectiveness.

Objectives

To determine the influence of hand dominance, gender, and body mass index (BMI) on HGS among university students in Ghana.

Method

In our cross-sectional study of 304 participants, height, weight, and BMI were measured using a stadiometer and weighing scale. Hand grip strength was assessed with a dynamometer. We compared HGS in dominant and non-dominant hands for males and females using a paired t-test and analysed the correlation between grip strength and weight, height, and BMI using Pearson's correlation coefficient.

Results

The mean HGS for right-hand dominant (RHD) male participants was 35.62 kg (± 7.36) for the right hand compared with 32.84 kg (± 7.36) for the left hand. For females RHD the mean HGS in the right hand was 24.60 kg (± 6.42) compared to 22.12 kg (± 5.37) in the left hand. The mean weight, height and BMI of participants were 62.86 kg (± 10.30), 1.67 m (± 0.09) and 22.9 kg/m2 (± 4.9), respectively. A significant relationship existed between HGS and height (r = 0.492; p < 0.01) as well as HGS and BMI (r = 0.290; p < 0.01). However, no notable connection was found between HGS and weight (r = 0.001; p = 0.982).

Conclusion

Hand grip strength was significantly stronger in the dominant hand of both males and females.

Clinical implications

Physiotherapists should test HGS objectively and quantitatively for use in disease evaluation, diagnosis, and therapy.

Determining classes of food items for health requirements and nutrition guidelines using Gaussian mixture models

Introduction

The identification of classes of nutritionally similar food items is important for creating food exchange lists to meet health requirements and for informing nutrition guidelines and campaigns. Cluster analysis methods can assign food items into classes based on the similarity in their nutrient contents. Finite mixture models use probabilistic classification with the advantage of taking into account the uncertainty of class thresholds.

Methods

This paper uses univariate Gaussian mixture models to determine the probabilistic classification of food items in the South African Food Composition Database (SAFCDB) based on nutrient content.

Results

Classifying food items by animal protein, fatty acid, available carbohydrate, total fibre, sodium, iron, vitamin A, thiamin and riboflavin contents produced data-driven classes with differing means and estimates of variability and could be clearly ranked on a low to high nutrient contents scale. Classifying food items by their sodium content resulted in five classes with the class means ranging from 1.57 to 706.27 mg per 100 g. Four classes were identified based on available carbohydrate content with the highest carbohydrate class having a mean content of 59.15 g per 100 g. Food items clustered into two classes when examining their fatty acid content. Foods with a high iron content had a mean of 1.46 mg per 100 g and was one of three classes identified for iron. Classes containing nutrient-rich food items that exhibited extreme nutrient values were also identified for several vitamins and minerals.

Discussion

The overlap between classes was evident and supports the use of probabilistic classification methods. Food items in each of the identified classes were comparable to allowed food lists developed for therapeutic diets. This data-driven ranking of nutritionally similar classes could be considered for diet planning for medical conditions and individuals with dietary restrictions.

Model Analysis and Experimental Study of Lower Limb Rehabilitation Training Device Based on Gravity Balance

More hemiplegia patients tend to use equipment for rehabilitation training due to the lack of physical therapists and the low effect of manual training. Nowadays, lower limb rehabilitation training devices for patients in grade 2 of the Medical Research Council (MRC-2) scale are still scarce and have some issues of poor autonomy and cannot relieve the muscle weakness of patients. To address these problems, a prototype based on gravity balance was designed with the combination of springs and linkages to enable patients to passively experience the rehabilitation training in the state of balancing the gravity of lower limbs. The motion of the mechanism was analyzed to obtain the functional relation between the motor rotation angle and the joints’ angle. Based on the principle of constant potential energy, a gravity balance mathematical model of the device was established, analyzed, and simulated. Moreover, through the training experiment, the results show that when subjects in three different weights were trained under the rehabilitation device with and without gravity balance, the required torques of the motor and EMG signal strength of the knee and hip joints decreased by a degree of significance, which verified the effectiveness of the device’s gravity balancing characteristics for MRC-2 patients.