Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management.

The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.

Treatment of chronic back pain using indirect vibroacoustic therapy: A pilot study.

BACKGROUND: Low frequency sound wave stimulation therapy has become increasingly popular in the rehabilitation fields, due to its ease, less fatiguing and time efficient application. OBJECTIVE: This 12-week pilot study examines the efficacy of applying low frequency sound wave stimulation (between 16-160 Hz) through both hands and feet on relieving pain and improving functional ability in patients with chronic back pain. METHODS: Twenty-three participants with chronic shoulder (eleven participants) or low back pain (twelve participants) underwent a 12-week vibration therapy program of three sessions per week. A low frequency sound wave device comprising four piezoelectric vibration-type tactile tranducers enclosed in separate 5-cm diameter circular plates, which generate sinusoidal vibratory stimuli at a frequency of 16-160 Hz, was used in this study. Primary outcome measure was pain sensation measured using the Visual Analogue Scale (P-VAS). The secondary outcome measures were pain-related disability measured using the pain disability index (PDI) and quality of life measured using the SF-12. RESULTS: At week 12, significant reductions in pain sensation and pain-related disability were observed, with mean reductions of 3.5 points in P-VAS and 13.5 points in the PDI scores. Sixty-five percent of the participants had a reduction of at least 3 points on the P-VAS score, while 52% participants showed a decrease of at least 10 points in the PDI score. Significant improvement was observed in the SF-12 physical composite score but not the mental composite score. CONCLUSIONS: The preliminary findings showed that passive application of low frequency sound wave stimulation therapy through both hands and feet was effective in alleviating pain and improving functional ability in patients with chronic back pain.

Mechanomyography responses characterize altered muscle function during electrical stimulation-evoked cycling in individuals with spinal cord injury.

BACKGROUND: Investigation of muscle fatigue during functional electrical stimulation (FES)-evoked exercise in individuals with spinal cord injury using dynamometry has limited capability to characterize the fatigue state of individual muscles. Mechanomyography has the potential to represent the state of muscle function at the muscle level. This study sought to investigate surface mechanomyographic responses evoked from quadriceps muscles during FES-cycling, and to quantify its changes between pre- and post-fatiguing conditions in individuals with spinal cord injury. METHODS: Six individuals with chronic motor-complete spinal cord injury performed 30-min of sustained FES-leg cycling exercise on two days to induce muscle fatigue. Each participant performed maximum FES-evoked isometric knee extensions before and after the 30-min cycling to determine pre- and post- extension peak torque concomitant with mechanomyography changes. FINDINGS: Similar to extension peak torque, normalized root mean squared (RMS) and mean power frequency (MPF) of the mechanomyography signal significantly differed in muscle activities between pre- and post-FES-cycling for each quadriceps muscle (extension peak torque up to 69%; RMS up to 80%, and MPF up to 19%). Mechanomyographic-RMS showed significant reduction during cycling with acceptable between-days consistency (intra-class correlation coefficients, ICC = 0.51-0.91). The normalized MPF showed a weak association with FES-cycling duration (ICC = 0.08-0.23). During FES-cycling, the mechanomyographic-RMS revealed greater fatigue rate for rectus femoris and greater fatigue resistance for vastus medialis in spinal cord injured individuals. INTERPRETATION: Mechanomyographic-RMS may be a useful tool for examining real time muscle function of specific muscles during FES-evoked cycling in individuals with spinal cord injury.

Mechanomyographic parameter extraction methods: an appraisal for clinical applications.

The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG) parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity.

The effectiveness of FES-evoked EMG potentials to assess muscle force and fatigue in individuals with spinal cord injury.

The evoked electromyographic signal (eEMG) potential is the standard index used to monitor both electrical changes within the motor unit during muscular activity and the electrical patterns during evoked contraction. However, technical and physiological limitations often preclude the acquisition and analysis of the signal especially during functional electrical stimulation (FES)-evoked contractions. Hence, an accurate quantification of the relationship between the eEMG potential and FES-evoked muscle response remains elusive and continues to attract the attention of researchers due to its potential application in the fields of biomechanics, muscle physiology, and rehabilitation science. We conducted a systematic review to examine the effectiveness of eEMG potentials to assess muscle force and fatigue, particularly as a biofeedback descriptor of FES-evoked contractions in individuals with spinal cord injury. At the outset, 2867 citations were identified and, finally, fifty-nine trials met the inclusion criteria. Four hypotheses were proposed and evaluated to inform this review. The results showed that eEMG is effective at quantifying muscle force and fatigue during isometric contraction, but may not be effective during dynamic contractions including cycling and stepping. Positive correlation of up to r = 0.90 (p < 0.05) between the decline in the peak-to-peak amplitude of the eEMG and the decline in the force output during fatiguing isometric contractions has been reported. In the available prediction models, the performance index of the eEMG signal to estimate the generated muscle force ranged from 3.8% to 34% for 18 s to 70 s ahead of the actual muscle force generation. The strength and inherent limitations of the eEMG signal to assess muscle force and fatigue were evident from our findings with implications in clinical management of spinal cord injury (SCI) population.