A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors.

Gait disorder is common among people with neurological disease and musculoskeletal disorders. The detection of gait disorders plays an integral role in designing appropriate rehabilitation protocols. This study presents a clinical gait analysis of patients with polymyalgia rheumatica to determine impaired gait patterns using machine learning models. A clinical gait assessment was conducted at KATH hospital between August and September 2022, and the 25 recruited participants comprised 18 patients and 7 control subjects. The demographics of the participants follow: age 56 years ± 7, height 175 cm ± 8, and weight 82 kg ± 10. Electromyography data were collected from four strained hip muscles of patients, which were the rectus femoris, vastus lateralis, biceps femoris, and semitendinosus. Four classification models were used-namely, support vector machine (SVM), rotation forest (RF), k-nearest neighbors (KNN), and decision tree (DT)-to distinguish the gait patterns for the two groups. SVM recorded the highest accuracy of 85% among the classifiers, while KNN had 75%, RF had 80%, and DT had the lowest accuracy of 70%. Furthermore, the SVM classifier had the highest sensitivity of 92%, while RF had 86%, DT had 90%, and KNN had the lowest sensitivity of 84%. The classifiers achieved significant results in discriminating between the impaired gait pattern of patients with polymyalgia rheumatica and control subjects. This information could be useful for clinicians designing therapeutic exercises and may be used for developing a decision support system for diagnostic purposes.

Design Validation of a Low-Cost EMG Sensor Compared to a Commercial-Based System for Measuring Muscle Activity and Fatigue.

Electromyography (EMG) sensors have been used for measuring muscle signals and for diagnosing neuromuscular disease. Available commercial EMG sensor are expensive and not easily available for individuals. The aim of the study is to validate our designed low-cost sensor against a well-known commercial system for measuring muscle activity and fatigue assessment. The evaluation of the designed system was done through a series of dynamic exercises performed by volunteers. Our low-cost EMG sensor and the commercially available system were placed on the vastus lateralis muscle to concurrently record the signal in a maximum voluntary contraction (MVC). The signal analysis was done using two validation indicators: Spearman's correlation, and intra-class cross correlation on SPSS 26.0 version. For the muscle fatigue assessment, the root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) indicators were used. The results at the peak and mean level muscle contraction intensity were computed. The relative agreement for the two systems was excellent at peak level muscle contraction range (ICC 0.74-0.92), average 0.83 and mean level muscle contraction intensity range (ICC 0.65-0.85) with an average of 0.74. The Spearman's correlation average was 0.76 with the range of (0.71-0.85) at peak level contraction, whiles the mean level contraction average was 0.71 at a range of (0.62-0.81). In determining muscle fatigue, the RMS and MAV showed increasing values in the time domain, while the MEF decreased in the frequency domain. Overall, the results indicated a good to excellent agreement of the two systems and confirmed the reliability of our design. The low-cost sensor also proved to be suitable for muscle fatigue assessment. Our designed system can therefore be implemented for rehabilitation, sports science, and ergonomics.

Clinical characteristics and outcome of the first 200 patients hospitalized with coronavirus disease-2019 at a treatment center in Abuja, Nigeria: a retrospective study.

Introduction: globally, the ravaging effect of the coronavirus disease-2019 (COVID-19), pandemic is evident on public health and the global economy. We aimed at describing the clinical characteristic and management outcome of COVID-19 patients in Abuja, Nigeria. Methods: we conducted a retrospective study by reviewing the hospital charts of the first 200 COVID-19 patients admitted at the isolation center, University of Abuja Teaching Hospital (UATH), Gwagwalada. Extracted data includes; demographic data, clinical symptoms, underlying comorbidities, and clinical outcomes. The outcome of interest was either discharged or died. Data was analyzed using the Statistical Package for Social Sciences (SPSS) version 20.0. Results: the median age was 45 years (range 2-84 years). Majority of the patients were males (66.5%). The most affected age group was 50-59 years (21%). Children and adolescents were least affected; less than 10 years constituted 2.5% and 10-19 years constituted 4.5%. The commonest symptoms at presentation were fever (94%) and cough (92%). Ninety-four patients (47%) had underlying comorbidities; the commonest was hypertension (36%). Based on disease severity; 126 (63%) had mild disease, 22 (11%) had moderate disease and 52 (26%) had severe disease. The commonest complication was Acute Respiratory Distress Syndrome (ARDS) seen in 29 (14.5%) patients. Out of the 200 cases managed, 189 (94.5%) were discharged in a stable condition while 11 (5.5%) died. Patients with under lying comorbidities had 9.6% death rate while those without comorbidities had 1.9% death rate. Conclusion: among Nigerian patients', COVID-19 affects males more than females while children and adolescents were least affected. The study highlighted the clinical features of COVID-19 patients. The overall mortality rate is low among Nigerian patients compared to patients in the USA and Europe. This study shows that advanced age, presence of underlying comorbidities and disease severity is associated with the risk of dying from COVID-19.

A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment.

Gait and posture studies have gained much prominence among researchers and have attracted the interest of clinicians. The ability to detect gait abnormality and posture disorder plays a crucial role in the diagnosis and treatment of some diseases. Microsoft Kinect is presented as a noninvasive sensor essential for medical diagnostic and therapeutic purposes. There are currently no relevant studies that attempt to summarise the existing literature on gait and posture abnormalities using Kinect technology. The purpose of this study is to critically evaluate the existing research on gait and posture abnormalities using the Kinect sensor as the main diagnostic tool. Our studies search identified 458 for gait abnormality, 283 for posture disorder of which 26 studies were included for gait abnormality, and 13 for posture. The results indicate that Kinect sensor is a useful tool for the assessment of kinematic features. In conclusion, Microsoft Kinect sensor is presented as a useful tool for gait abnormality, postural disorder analysis, and physiotherapy. It can also help track the progress of patients who are undergoing rehabilitation.