**1. Introduction**

Individuals with limb amputation or congenital limb deficits or stroke often have difficulty in performing simple and complex daily life activities that involve the use of their upper extremity (UE). They often depend on the healthy part of their body to compensate for a lost limb, which can greatly

affect body posture and symmetry alignment. They also experience phantom limb sensation (which is usually painful), fatigue, and depression among others, which can cause emotional and psychological damage. In addition, majority of individuals with UE disability normally feel inferior or perhaps rejected in the society because they can hardly cope with certain physical daily life activities [1–3].

Re-integrating this category of persons into the society would require the development of a smart and intelligent rehabilitation robotic system [2–5]. Such intelligent robotic system normally incorporate less computational control algorithms that operate on symmetric principle and attract low memory and processor requirement, thus, aiding the realization of portable rehabilitation device that could be worn by amputees to help restore their arm functions [3–6]. Notably, such symmetrical principle play a significant role when it comes to the dynamics of controlling the prosthetic device during activities of daily living [7]. At the forefront of this technology is the pattern recognition based prostheses that seamlessly decode multiple patterns of targeted limb movements from measured bioelectrical data and provide multiple degrees of freedom arm function in an intuitive fashion [1,3,4]. Typically, the pattern recognition strategy involves extraction of highly informative feature sets from the measured surface electromyogram (sEMG) data, which are applied to a machine learning model for limb movement intent decoding.

Afterwards, the deciphered movement intents are coded into control commands that drives the intelligently smart prostheses in a way similar to the natural human arm [6–10]. The pattern recognition based control strategy consist of sequentially connected phases in which the machine learning algorithm and feature extraction phases are well-thought-out as important with the latter being the most significant. Hence, the feature extraction approach adopted would either potentially improve or degrade the overall performance of device, and it has been proven by several previous studies [5,11,12]. In other words, developing an intelligently smart pattern recognition based prostheses would require the integration of appropriate feature extraction technique. Moreover, extracting features from segmented sEMG data as against the entire length of measured data would expedite the response rate of the prosthetic device in real-life applications, and the sliding window segmentation technique has been widely employed due to its dense decision stream attribute [13]. Integral elements of the sliding window segmentation scheme includes the window length and increment parameters, which would normally influence the characteristics of the extracted feature set (in terms of stability and accuracy) [12,13]. These parameters have direct impact on the extracted features and as well influence the characteristics of the prostheses controller in terms of its delay, since the delay is a function of the computation time associated with the extracted features [14,15]. Therefore, the overall effect of the feature extraction scheme and windowing parameters should not be underestimated if the goal is to realize an intelligently smart prostheses that would be clinically viable. Towards addressing the above highlighted problem, Menon et al. examined the effect of sliding window segmentation on classification accuracy using sEMG data measured from different groups of participants (able-bodied, partial hand and transhumeral participants). They found that the impact of window length on classification performance does not depend on the number of electrodes channels irrespective of the participants group. It was also discovered that the window increment has no direct effect on the classification accuracy regardless of the window length, number of electrode channels considered and the amputation status of participants, [16]. Meanwhile, in the Englehart and Hudgins study, they demonstrated the relationship between the analysis window length and classification error and it was realized that the mean classification error increase with short window length [13]. Smith et al. in their work investigated the relationship between window length, classification accuracy and controller delay and their result showed that the choice of window length is an important factor that could, either improve or degrade the performance of pattern recognition based prostheses control [14]. Other researchers demonstrated that to realize acceptable accuracy with reasonable controller delay, window lengths in the range of 100 ms~125 ms should be considered and that the controller delay in real-time operation should be lower than 200 ms. Also, window lengths between the range of 100 ms~300 ms [12], and 300 ms~400 ms [13] were previously recommended [17–21]. However, no singular study has

considered investigating the interrelation effects amongst classification accuracy, computation time, robustness to external noise, and number of electrode channels, across different window parameters and multiple feature sets for EMG-Pattern recognition (EMG-PR) systems. In other words, it is unclear how these multiple dynamic factors would influence movement intent decoding, which represent an essential control input for intelligently smart EMG-PR based prostheses. Additionally, investigations on the tradeoff amongst these factors across combinations of window parameters and feature sets has seldom been considered particularly when using sEMG recordings from amputees for movement intent decoding, thus, constituting a core research gap in the field of intelligently smart prostheses.

In this study, we systematically investigated the interrelations and impact of various windowing parameters on a range of feature sets when applied to decode multiple-patterns of targeted limb movement intents across amputees (who are the final users of the prosthetic device) and healthy subjects. Afterwards, the performance evaluation of the extracted features were carried out with respect to classification error, computation time, robustness to external noise and number of recording channels, in the context of machine learning based EMG-PR movement intent classifier.

Interestingly, the experiment and analyses were conducted using sEMG data acquired from both able-bodied-subjects, transradial, and transhumeral amputees, which would ensure the potential application of the study outcomes in clinical and commercial settings. The main contributions of this study are in three folds:


In summary, the outcomes of this study are capable of providing researchers and developers with proper insight on how to best select features and/or windowing parameters to achieve optimal movement intent decoding in EMG pattern recognition systems. Furthermore, it may spur potential advancement in smart prosthetic control system and other areas that employs pattern recognition based concept for the provision of smart healthcare services [22–24].
