The purpose of this paper is to develop an effective method to identify upper limb motions based on EMG signal for community rehabilitation. The method will be applicable to the control system in the rehabilitation equipment and provide objective data for quantitative assessment. The recognition goal sets of upper limb motion are constructed by decomposing assessment activities of activity of daily living scale (ADL). The recognition feature vector space is established by Variance (VAR), Mean Absolute Value (MAV), the fourth-order Autoregressive (the 4thAR), Zero Crossings (ZC’s), integral EMG (IEMG), and Root Mean Square (RMS), and various feature sets are extracted to get the best classification. Locally linear embedding (LLE) algorithm is used to reduce the computational complexity, and upper limb motions about shoulder, elbow and wrist are quickly classified through extreme leaving machine (ELM), which obtained the average accuracy of 98.14%, 98.61% and 94.77%, respectively. Furthermore, when ELM is compared with Back-propagation (BP) and Support vector machine (SVM), it has performed relatively better than BP and SVM. The results show that the validity of the mixed model for recognition is verified. In addition, the method can also provide a basis for recognition and assessment of the angle of upper limb joint in the next study.

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Accurate comprehension of safety signs plays a critical part in warning construction workers of potential work hazards. However, existing studies have rarely investigated construction workers' comprehension of safety signs at construction sites. Moreover, existing evaluation methods are generally based on subjective behavior tests, questionnaires, and interviews. Therefore, this study examined the effects of semantic congruity on the comprehension of safety signs, including two sign types (prohibition vs. warning signs) and two conditions (semantic congruence vs. incongruence), combining event‐related potentials and time‐frequency analysis measurements. Adopting the S1‐S2 paradigm, electroencephalogram data were recorded when participants decided whether S1 and S2 were semantically congruent or not. Results showed that the semantically incongruent safety sign‐word pairs elicited larger N400 amplitudes and increased theta (3–8 Hz) power in 300–420 ms. The amplitude of N400 in the semantically incongruent condition of the warning sign‐word pairs was more negative than that for the prohibition sign‐word pairs, while there were no significant differences between the prohibition and warning sign‐word pairs in the semantically congruent condition. A greater late positive potential (LPP) (550–750 ms) was also elicited in the semantically incongruent safety sign‐word pairs, which was different from previous studies that observed larger LPP in congruent conditions. These results suggest complicated cognitive mechanisms of safety sign comprehension in construction workers. This study extends safety sign comprehension research by using electrophysiological approaches and provides useful indicators for researchers or safety engineers to measure the semantic congruity of proposed sign designs.

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This paper presents an LLE-ELM recognition model, which can identify upper limb motions with EMG signal acquisition system. In this study, the recognizing target set is established by the different angles of the two joint motions, surface electromyogram (sEMG) of shoulder and elbow actions are used as information sources, and the time-domain feature of the sEMG is formed to the characteristic vector space. LLE is used to reduce the difficulty of data calculation, and ELM is used to improve the speed and accuracy of motion recognition. Result shows that the validity of the LLE-ELM mixed model to identify the joint range of motions is verified.