Laboratory tests validate the proper operation of the integrated technologies, highlighting a low latency and reasonable accuracy. Different operation modes are supported by a hierarchical control strategy, allowing operation in autonomous mode, remote control mode, or in a leader-follower mode. The proposed exoskeleton is a 1-DoF system that allows flexion-extension at the elbow joint, where the chosen materials render it compact. This paper focuses on the design, development, and preliminary testing of a wearable robotic exoskeleton prototype with autonomous Artificial Intelligence-based control, processing, and safety algorithms that are fully embedded in the device. Modern approaches such as robotic-assisted rehabilitation provide decisive factors for effective motor recovery, such as objective assessment of the progress of the patient and the potential for the implementation of personalized training plans. Neuromotor rehabilitation and recovery of upper limb functions are essential to improve the life quality of patients who have suffered injuries or have pathological sequels, where it is desirable to enhance the development of activities of daily living (ADLs). Compared with existing MDD detection methods with the best accuracy of 0.9840 and $$F_1$$ F 1 score of 0.97, our proposed framework achieves the state-of-the-art MDD detection performance.ĭevelopment of this MDD detection framework can be potentially deployed into a medical system to aid physicians to screen out MDD patients. Meanwhile, the regression determination coefficient $$R^2$$ R 2 for MDD severity assessment is up to 0.9479.
The accuracy and $$F_$$ F 1 score are up to 0.9895 and 0.9846, respectively. Finally, we employ widely used machine learning methods of support vector machine (SVM), logistic regression (LR), and linear regression (LNR) for MDD detection with the merit of feature interpretability.Įxperiment results show that our proposed MDD detection framework achieves competitive results. Then, a two-stage feature selection method named PAR is presented with the sequential combination of Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), where the advantages lie in minimizing the feature searching space. First of all, we derive highly MDD-correlated features, calculating the ratio of extracted features from EEG signals at frequency bands between $$\beta$$ β and $$\alpha$$ α. In this work, we present a novel automatic MDD detection framework based on EEG signals. Major depressive disorder (MDD) is a common mental illness, characterized by persistent depression, sadness, despair, etc., troubling people’s daily life and work seriously. Additionally, the experimental results show that the proposed system requires minimal power and is both sensitive and stable. The response time is stably between 1 and 2 s, and, as indicated by the consistent change of digital value, our systems clearly diminishes muscle fatigue. As indicated by the consistent change of digital value, muscle fatigue was clearly diminished using this system.Įxperiments show that environmental factors have little effect on the response time and accuracy of the system. With the help of the proposed system, human muscle fatigue status can be monitored in real-time, and the recovery vibration motor status can be optimized according to muscle activity state.Įnvironmental factors had little effect on the response time and accuracy of the system, and the response time was stable between 1 and 2 s. Muscle fatigue can be detected by surface electromyography signals and monitored in real-time via a wireless network.
The ESP8266 is employed as the main controller and communicator, and PWM technology is employed to achieve adaptive muscle recovery. To facilitate muscle fatigue detection, a pulse width modulation (PWM) and ESP8266-based fatigue detection and recovery system is introduced in this paper to help alleviate muscle fatigue. While there are a number of different health-related problems encountered in daily life, muscle fatigue is a common problem encountered by many. Internet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients.
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