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Abstract-Effective treatments for multiple sclerosis (MS)-associated central fatigue have not been established. Surface functional electrical stimulation (FES), which can challenge the peripheral neuromuscular system without overloading the central nervous system, is a relatively safe therapeutic strategy. We investigated the effect of 8 weeks of surface FES training on the levels of general, central, and peripheral fatigue in MS patients. Seven of nine individuals with MS (average age: 42.86 +/-13.47 years) completed 8 weeks of quadriceps muscle surface FES training. Maximal voluntary contraction, voluntary activation level, twitch force, General Fatigue Index (FI), Central Fatigue Index (CFI), Peripheral Fatigue Index, and Modified Fatigue Impact Scale (MFIS) scores were determined before and after training. The results showed that FI (p = 0.01), CFI (p = 0.02), and MFIS (p = 0.02) scores improved significantly after training. Improvements in central fatigue contributed significantly to improvements in general fatigue (p < 0.01). The results of the current study showed that central fatigue was a primary limitation in patients with MS during voluntary exercise and that 8 weeks of surface FES training for individuals with MS led to significantly reduced fatigue, particularly central fatigue.

Immediate monitoring of the conditions of the grinding wheel during the grinding process is important because it directly affects the surface accuracy of the workpiece. Because the variation in machining sound during the grinding process is very important for the field operator to judge whether the grinding wheel is worn or not, this study applies artificial intelligence technology to attempt to learn the experiences of auditory recognition of experienced operators. Therefore, we propose an intelligent system based on machining sound and deep learning to recognize the grinding wheel condition. This study uses a microphone embedded in the grinding machine to collect audio signals during the grinding process, and extracts the most discriminated feature from spectrum analysis. The features will be input the designed CNNs architecture to create a training model based on deep learning for distinguishing different conditions of the grinding wheel. Experimental results show that the proposed system can achieve an accuracy of 97.44%, a precision of 98.26% and a recall of 96.59% from 820 testing samples. INDEX TERMS Grinding wheel wear, intelligent system, machining sound, audio signals, deep learning. I. INTRODUCTION CHENG-HSIUNG LEE received the B.I.M. and M.I.M. degrees in information management from the Chaoyang University of Technology, in 2002 and 2004, respectively, and the Ph.D. degree in computer science and engineering from the

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The fatigability of paralyzed muscle limits its ability to deliver physiological loads to paralyzed extremities during repetitive electrical stimulation. The purposes of this study were to determine the reliability of measuring paralyzed muscle fatigue and to develop a model to predict the temporal changes in muscle fatigue that occur after spinal cord injury (SCI). Thirty-four subjects underwent soleus fatigue testing with a modified Burke electrical stimulation fatigue protocol. The between-day reliability of this protocol was high (intraclass correlation, 0.96). We fit the fatigue index (FI) data to a quadratic-linear segmental polynomial model. FI declined rapidly (0.3854 per year) for the first 1.7 years, and more slowly (0.01 per year) thereafter. The rapid decline of FI immediately after SCI implies that a "window of opportunity" exists for the clinician if the goal is to prevent these changes. Understanding the timing of change in muscle endurance properties (and, therefore, load-generating capacity) after SCI may assist clinicians when developing therapeutic interventions to maintain musculoskeletal integrity. Keywords electrical stimulation; muscle fibers; plasticity; reliability; spinal cord injury Paralysis after spinal cord injury (SCI) deprives the musculoskeletal system of important stresses necessary to maintain the health of the paralyzed extremities. 30,33 Without these normal stresses, muscle atrophy and severe osteoporosis develop rapidly. 30 Electrically induced muscle contractions are an integral part of contemporary methods designed to place physiological stresses on the paralyzed extremities. 3,4,6,17,24 However, as muscle adapts after SCI it loses its fatigue resistance 29,36 and becomes a less effective source for generating stress to the paralyzed extremities. Establishing the time course of muscle fatigability may aid rehabilitation practitioners when developing comprehensive rehabilitation plans to maintain musculoskeletal properties after SCI. To our knowledge, no previous study has developed a model based on longitudinal data to predict the temporal changes in muscle fatigue after SCI.The chronically paralyzed soleus muscle transforms to highly fatigable fibers, while the acutely paralyzed muscle is fatigue resistant. 29 optimum stimulation parameters to attenuate this rapid fatigue in chronically paralyzed muscle. 9,13,15,37 Rarely has the goal been to prevent the muscle from becoming highly fatigable by intervening early after SCI, before the fibers become fatigable. Accordingly, a window likely exists after SCI that represents a key period during which an intervention would need to be initiated in order to maintain the endurance properties of paralyzed muscle.The most direct approach to quantifying muscle fatigue is to measure the torque generated at the beginning and end of a series of repetitive contractions. The quotient of these torques, the Burke fatigue index (FI), differs according to the type of motor unit being activated (being higher for slow and lower for fast...