###### Abstract  
Developing a robust machine-learning algorithm to detect hand motion is one of the most challenging aspects of prosthetic hands and exoskeleton design. Machine-learning methods that considered sex differences were used to identify and describe hand movement patterns in healthy individuals. To this purpose, surface Electromyographic (sEMG) signals have been acquired from muscles in the forearm and hand. The results of statistical analysis indicated that most of the same muscle pairs in the right hand (females and males) showed significant differences during the six hand movements. Time features were used an as input to machine-learning algorithms for the recognition of six gestures. Specifically, two types of hand-gesture recognition methods that considered sex differences(differentiating sex datasets and adding a sex label)were proposed and applied to the k-nearest neighbor (k-NN), support vector machine (SVM) and artificial neural network (ANN) algorithms for comparison. In addition, a t-test statistical analysis approach and 5-fold cross validation were used as complements to verify whether considering sex differences could significantly improve classification performance. It was demonstrated that considering sex differences can significantly improve classification performance. The ANN algorithm with the addition of a sex label performed best in movement classification (98.4% accuracy). In the future, hand movement recognition algorithms considering sex differences could be applied to control systems for prosthetic hands or exoskeletons. hand movements; sex differences; machine learning artificial neural network exoskeletons; surface electromyography (sEMG) +
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The exoskeleton device could support fingers and hands and also could provide direct control of the hand joints. The functions of exoskeletons range from knob movements and assistance of broad grasp, such as the Haptic-Knob and HWARD devices [14; 15], to highly specialized devices for controlling fingers and hand movements, such as the CyberGrasp, the Hand-of-Hope [16], the HEXXOR [17], the Soft RoboticGlove [18], the Gloreha [19] and the ReHand [20]. The control system of the prosthetic hand and exoskeleton can drive the system response through their own electrical signals [21]. The electroencephalography (EEG) and electromyography (EMG) signals both can directly reflect the patients' control and intention for movement. In particular, surface electromyography (sEMG) is valuable in the study of prosthetic hands and assisted rehabilitation robots [22; 23] due to its advantages of simple acquisition and processing, wireless sensing, wearable electrodes and the ability to give information on movement intention 50-100 ms before the actual movement [24]. For instance, the surface electrodes of two muscles on the forearm, the extensor digitorum communis and the flexor digitorum superficialis, can be used to partly record the flexion and extension intentions of the fingers [25]. However, in practical applications, sEMG signals are highly dependent on the instrumentation, methods and procedures used in the system. At the same time, the signal acquisition process generates different degrees of artifacts and crosstalk [25]. Thus, robust and accurate machine learning methods need to be developed for detecting hand movements to control exoskeletons and prosthetic hands in real time. In previous studies, features or characteristics in the time domain, frequency domain and time-frequency domain had been used to detect hand motion. However, the time-domain features were considered to perform better than the frequency domain or time-frequency domain features [26; 27]. In the time domain, the most used features are: Mean Absolute Value (MAV), Waveform Length (WL), Willison Amplitude (WAMP) and Zero-Crossing (ZC) [28; 29; 30]. Common gestures were classified by [PERSON] et al. [30], using only time-domain feature values, and the accuracy can be up to 90.57%.