Effect of Functional Electrical Stimulation in Convalescent Stroke Patients: A Multicenter, Randomized Controlled Trial

Matsumoto, Shûji; Shimodozono, Megumi; Noma, Tomokazu; Miyara, Kodai; Onoda, Tetsuya; Ijichi, Rina; Shigematsu, Takaaki; Satone, Akira; Okuma, Hidenobu; Seto, Makiko; Taketsuna, Masanori; Kaneda, Hideaki; Matsuo, Motoaki; Kojima, Shinsuke

doi:10.3390/jcm12072638

Cited by 4 publications

References 29 publications

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Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM

Rostovski,

Ahmadilivani,

Krivošei

et al. 2024

Communications in Computer and Information Science

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Anomaly detection and fall prevention represent one of the key research areas within gait analysis for patients suffering from neurological disorders. Deep Learning has penetrated into healthcare applications, encompassing disease diagnosis and anomaly prediction. Connected wearable medical sensors are emerging due to computationally expensive machine learning tasks, which traditionally require use of remote PC or cloud computing. However, to reduce needs for wireless communication channel throughput, for data processing latency, and increase service reliability and safety, on device machine learning is gaining attention. This paper presents an innovative approach that leverages one dimensional convolutional neural network (1D-CNN) and long-short term memory (LSTM) neural network for the real-time detection of abnormal gait patterns during the step. Real-time anomaly detection pertains to the algorithm’s ability to promptly detect true gait abnormality occurrence during the swing phase of an ongoing step.For the experiments, we have collected eight different common gait anomalies, simulated by 22 persons, using motion sensors containing multidimensional inertial measurement units (IMUs).Results have demonstrated that the proposed 1D-CNN-AD algorithm achieves an average accuracy of 95% and an average F1-score of 88% for all gait types and can run in true real-time. Average earliness for 1D-CNN-AD algorithm was 0.6 s, which is mid-swing phase of the step. Proposed LSTM-AD algorithm achieved average accuracy of 87% and average F1-score of 70% for all gait types.

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Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM

Rostovski,

Ahmadilivani,

Krivošei

et al. 2024

Communications in Computer and Information Science

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Quantifying Treatments as Usual and with Technologies in Neurorehabilitation of Individuals with Spinal Cord Injury

Tamburella,

Lorusso,

Merone

et al. 2024

Healthcare

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Several technologies have been introduced into neurorehabilitation programs to enhance traditional treatment of individuals with Spinal Cord Injury (SCI). Their effectiveness has been widely investigated, but their adoption has not been properly quantified. The aim of this study is to assess the distribution of conventional (Treatment As Usual—TAU) and technology-aided (Treatment With Technologies—TWT) treatments conveniently grouped based on different therapeutic goals in a selected SCI unit. Data from 104 individuals collected in 29 months were collected in a custom database and categorized according to both the conventional American Impairment Scale classification and a newly developed Multifactor (MF) clustering approach that considers additional sources of information (the lesion level, the level of independence in the activities of daily living, and the hospitalization duration). Results indicated an average technology adoption of about 30%. Moreover, the MF clusters were less overlapped, and the differences in TWT adoption were more pronounced than in AIS-based clustering. MF clustering was capable of grouping individuals based both on neurological features and functional abilities. In particular, individuals with motor complete injuries were grouped together, whereas individuals with sensorimotor incomplete SCI were collected separately based on the lesion level. As regards TWT adoption, we found that in the case of motor complete SCI, TWT for muscle tone control and modulation was mainly selected (about 90% of TWT), while the other types of TWT were seldom adopted. Even for individuals with incomplete SCI, the most frequent rehabilitation goal was muscle tone modulation (about 75% of TWT), regardless of the AIS level, and technologies to improve walking ability (about 12% of TWT) and balance control (about 10% of TWT) were mainly used for individuals with thoracic or lumbar lesions. Analyzing TAU distribution, we found that the highest adoption of muscle tone modulation strategies was reported in the case of individuals with motor complete SCI (about 42% of TAU), that is, in cases when almost no gait training was pursued (about 1% of TAU). In the case of cervical motor incomplete SCI, compared to thoracic and lumbar incomplete SCI, there was a greater focus on muscle tone control and force recruitment in addition to walking training (38% and 14% of TAU, respectively) than on balance training. Overall, the MF clustering provided more insights than the traditional AIS-based classification, highlighting differences in TWT adoption. These findings suggest that a wider overview that considers both neurological and functional characteristics of individuals after SCI based on a multifactor analysis could enhance the personalization of neurorehabilitation strategies.

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Function Electrical Stimulation Effect on Muscle Fatigue Based on Fatigue Characteristic Curves of Dumbbell Weightlifting Training

Sun,

Xu,

et al. 2024

Cyborg Bionic Syst

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The parameter setting of functional electrical stimulation (FES) is important for active recovery training since it affects muscle health. Among the FES parameters, current amplitude is the most influential factor. To explore the FES effect on the maximum stimulation time, this study establishes a curve between FES current amplitude and the maximum stimulation time based on muscle fatigue. We collect 10 subjects’ surface electromyography under dumbbell weightlifting training and analyze the muscle fatigue state by calculating the root mean square (RMS) of power. By analyzing signal RMS, the fatigue characteristic curves under different fatigue levels are obtained. According to the muscle response under FES, the relationship curve between the current amplitude and the maximum stimulation time is established and FES parameters’ effect on the maximum stimulation time is obtained. The linear curve provides a reference for FES parameter setting, which can help to set stimulation time safely, thus preventing the muscles from entering an excessive fatigue state and becoming more active to muscle recovery training.

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Effect of Functional Electrical Stimulation in Convalescent Stroke Patients: A Multicenter, Randomized Controlled Trial

Cited by 4 publications

References 29 publications

Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM

Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM

Quantifying Treatments as Usual and with Technologies in Neurorehabilitation of Individuals with Spinal Cord Injury

Function Electrical Stimulation Effect on Muscle Fatigue Based on Fatigue Characteristic Curves of Dumbbell Weightlifting Training

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