**2. Materials and Methods**

The EMG-PR framework, adopted in this study, is shown in Figure 1. The process begins with the acquisition of the EMG signals, followed by the preprocessing of the signals to remove motion artifacts and power line interference. The resulting filtered EMG signal is segmented using a sliding analysis window technique. This step is often recommended to improve the efficiency of the subsequent processes which typical involves feature extraction and classification. Thereafter, each feature extraction method, considered in this study, is extracted from the analysis window, and the classifier is employed to decode the motion intent based on the extracted features.

*Symmetry* **2020**, *12*, x FOR PEER REVIEW 4 of 20

**Figure 1.** The block diagram of the electromyogram based pattern recognition control model. **Figure 1.** The block diagram of the electromyogram based pattern recognition control model.

#### *2.1. Participant Information 2.1. Participant Information*

In this study, a total of twelve subjects participated in the sEMG data measurement experiments. Five out of the recruited participants are fully-limbed subjects also referred to as able-bodied subjects while the remaining seven are arm amputees. Their ages range between 20~28 years and they all right-hand dominated. For the seven amputees, five of them had transradial amputation while the other two had transhumeral amputation. Prior to their inclusion in the study, their residual limbs were carefully examined to ensure appropriate conformity with the study objectives. Firstly, their residual limb muscles were carefully checked to ensure that they had no neuromuscular disorder. Secondly, the amputees were asked to perform a number targeted limb movements in a random sequence during which the myoelectric activities of their residual muscles were visualized, and afterwards certifies as being okay. Meanwhile, the amputees all have unilateral amputation with three of them having core experience in the usage of myoelectrically driven prostheses. Prior to the commencement of this experiment, the subjects were made to understand the aim of study, and they all consented and gave permission for the publication of their photographs/data for scientific purposes. Afterwards, the study protocol was approved by the Shenzhen Institutes of Advanced Technology Institutional Review Board, Chinese Academy of Sciences, China. In this study, a total of twelve subjects participated in the sEMG data measurement experiments. Five out of the recruited participants are fully-limbed subjects also referred to as able-bodied subjects while the remaining seven are arm amputees. Their ages range between 20~28 years and they all right-hand dominated. For the seven amputees, five of them had transradial amputation while the other two had transhumeral amputation. Prior to their inclusion in the study, their residual limbs were carefully examined to ensure appropriate conformity with the study objectives. Firstly, their residual limb muscles were carefully checked to ensure that they had no neuromuscular disorder. Secondly, the amputees were asked to perform a number targeted limb movements in a random sequence during which the myoelectric activities of their residual muscles were visualized, and afterwards certifies as being okay. Meanwhile, the amputees all have unilateral amputation with three of them having core experience in the usage of myoelectrically driven prostheses. Prior to the commencement of this experiment, the subjects were made to understand the aim of study, and they all consented and gave permission for the publication of their photographs/data for scientific purposes. Afterwards, the study protocol was approved by the Shenzhen Institutes of Advanced Technology Institutional Review Board, Chinese Academy of Sciences, China.

## *2.2. EMG Data Measurement 2.2. EMG Data Measurement*

The commonly utilized sEMG data recording device known as Trigno wireless recording system (Developed by Delsys Inc., a company based in Boston, MA, USA) was employed for the acquisition The commonly utilized sEMG data recording device known as Trigno wireless recording system (Developed by Delsys Inc., a company based in Boston, MA, USA) was employed for the acquisition of

the required sEMG signals. To determine the number of needed sEMG electrodes, we firstly examined different electrode configurations that involved the placement of 4~8 sensors on the forearm region. Afterwards, we realized that a total of six sensors would be sufficient to acquire high-quality recordings from which multiple-patterns of targeted limb movements could be adequately decoded. Each of the sensor contains 4 silver-bars that integrates three-axis accelerometer to capture arm dynamics and mechanomyogram signals. Meanwhile, the six sensors were configured to measure only sEMG without capturing the eighteen-channel mechanomyogram data, since we are only interested in analyzing the participants' limb movement intent from the sEMG signals. Although, there are other EMG measurement devices, but we decided to use the Trigno wireless recording system because it easy to use, it allows the recorded signals to be visualized in real-time which enables us to assess the signal quality, and it has wireless capability, that does not constrain the subjects during the experiment. *Symmetry* **2020**, *12*, x FOR PEER REVIEW 5 of 20 forearm region. Afterwards, we realized that a total of six sensors would be sufficient to acquire highquality recordings from which multiple-patterns of targeted limb movements could be adequately decoded. Each of the sensor contains 4 silver-bars that integrates three-axis accelerometer to capture arm dynamics and mechanomyogram signals. Meanwhile, the six sensors were configured to measure only sEMG without capturing the eighteen-channel mechanomyogram data, since we are only interested in analyzing the participants' limb movement intent from the sEMG signals. Although, there are other EMG measurement devices, but we decided to use the Trigno wireless recording system because it easy to use, it allows the recorded signals to be visualized in real-time which enables us to assess the signal quality, and it has wireless capability, that does not constrain

Haven determined the electrode configurations, the placement of the sensors was preceded by palpation of the remaining arm muscles in the amputee subjects to locate their belly and length as indicated in previous studies [24,25]. Afterwards, the sensors were placed over the skin area underlying the identified arm muscles in a symmetrical manner across both arms with the aid of adhesive (Figure 2a). That is, two out of the six sEMG sensors were placed on the extensor and flexor arm muscles while the remaining four sensors were positioned about 2–3 cm around the elbow crease as shown in Figure 2a,b. Notably, the symmetrical concept adopted in placing the electrode across both arms would enable the participant's intact arm to guide the amputated arm in adequately eliciting their movement intent during the experiment, which would lead to the recording of EMG signals with high neural information for movement intent decoding (Figure 2a). Prior to the sensor placement, the sensor sites mapped out on the participants' skin surface were thoroughly wiped using alcohol pads that takes off dry-dermis and skin-oil, which may affect the recorded signal's quality. For participants with unduly dry skin, the skin cells were extricated via tapping of the site with medical tapes to guarantee good electrode-skin contact. After ensuring proper electrode placement and good experimental condition, the subjects were presented with an audio prompt to guide them in performing all the classes of targeted upper-limb movements in a sequential order that includes: wrist movements (wrist flexion/extension/pronation/supination), hand movements (hand close/open) as shown in Figure 2c. the subjects during the experiment. Haven determined the electrode configurations, the placement of the sensors was preceded by palpation of the remaining arm muscles in the amputee subjects to locate their belly and length as indicated in previous studies [24,25]. Afterwards, the sensors were placed over the skin area underlying the identified arm muscles in a symmetrical manner across both arms with the aid of adhesive (Figure 2a). That is, two out of the six sEMG sensors were placed on the extensor and flexor arm muscles while the remaining four sensors were positioned about 2–3 cm around the elbow crease as shown in Figure 2a,b. Notably, the symmetrical concept adopted in placing the electrode across both arms would enable the participant's intact arm to guide the amputated arm in adequately eliciting their movement intent during the experiment, which would lead to the recording of EMG signals with high neural information for movement intent decoding (Figure 2a). Prior to the sensor placement, the sensor sites mapped out on the participants' skin surface were thoroughly wiped using alcohol pads that takes off dry-dermis and skin-oil, which may affect the recorded signal's quality. For participants with unduly dry skin, the skin cells were extricated via tapping of the site with medical tapes to guarantee good electrode-skin contact. After ensuring proper electrode placement and good experimental condition, the subjects were presented with an audio prompt to guide them in performing all the classes of targeted upper-limb movements in a sequential order that includes: wrist movements (wrist flexion/extension/pronation/supination), hand movements (hand close/open) as shown in Figure 2c.

**Figure 2.** Pre-experimental settings showing the placement of surface EMG electrodes on the residual limb of a representative amputee and healthy subject's limb alongside the active limb movement classes considered in the study. (**a**) Symmetrical placement of the wireless EMG signal sensors on the intact and amputated arm muscles of a representative amputee, (**b**) EMG electrode placement on the forearm of a representative healthy subject, (**c**) The classes of active targeted limb movements considered in the study. Note that: HO, HC, WF, WE, WP, WS, denotes hand open, hand close, wrist flexion, wrist extension, wrist pronation, and wrist supination, respectively. **Figure 2.** Pre-experimental settings showing the placement of surface EMG electrodes on the residual limb of a representative amputee and healthy subject's limb alongside the active limb movement classes considered in the study. (**a**) Symmetrical placement of the wireless EMG signal sensors on the intact and amputated arm muscles of a representative amputee, (**b**) EMG electrode placement on the forearm of a representative healthy subject, (**c**) The classes of active targeted limb movements considered in the study. Note that: HO, HC, WF, WE, WP, WS, denotes hand open, hand close, wrist flexion, wrist extension, wrist pronation, and wrist supination, respectively.

Following the audio prompt, the subjects were required to perform muscle contractions

Following the audio prompt, the subjects were required to perform muscle contractions conforming to the above described classes of targeted arm movements in which each movement was maintained for 5 s. And a rest session of 5 s was introduced between two consecutive classes of active movements to prevent the subjects from having fatigue. Meanwhile, each movement class got repeated five times, leading to 25 s of active EMG signal recordings and 20 s of rest session per experimental trial. *Symmetry* **2020**, *12*, x FOR PEER REVIEW 6 of 20 repeated five times, leading to 25 s of active EMG signal recordings and 20 s of rest session per experimental trial.

## *2.3. Preprocessing of the Measure sEMG Data 2.3. Preprocessing of the Measure sEMG Data*

The sEMG data were obtained during the experimental sessions at a sampling frequency of 1024 Hz, and then stored for further processing. The raw signals were firstly filtered using a 5th-Order Butterworth band pass filter designed with frequency in the range of 20 Hz~500 Hz to enable the extraction of useful components of the signals. Also, power line interferences were eliminated from the filtered signal using 50 Hz notch filter. It should be noted that recorded sEMG signals for all the subjects were preprocessed and analyzed offline using MATLAB version R2017b (Mathworks, Natick, MA, USA) programming tool. The sEMG data were obtained during the experimental sessions at a sampling frequency of 1024 Hz, and then stored for further processing. The raw signals were firstly filtered using a 5th-Order Butterworth band pass filter designed with frequency in the range of 20 Hz~500 Hz to enable the extraction of useful components of the signals. Also, power line interferences were eliminated from the filtered signal using 50 Hz notch filter. It should be noted that recorded sEMG signals for all the subjects were preprocessed and analyzed offline using MATLAB version R2017b (Mathworks, Natick, MA, USA) programming tool. Considering each class of movement, the recorded myoelectric signal is made up of five trials.

Considering each class of movement, the recorded myoelectric signal is made up of five trials. With a careful observation, the signals were partitioned into contraction/non-contraction segments for each class of targeted movement. To accomplish this task, signals from channels that have clear muscular activities, with respect to the baseline, were visually chosen and combined to obtain an average data stream, which would be needed in the subsequent stage. This process was realized based on the onset and offset times of each muscular activity from a representative channel that is applied to the signals of the other 5 channels. With a careful observation, the signals were partitioned into contraction/non-contraction segments for each class of targeted movement. To accomplish this task, signals from channels that have clear muscular activities, with respect to the baseline, were visually chosen and combined to obtain an average data stream, which would be needed in the subsequent stage. This process was realized based on the onset and offset times of each muscular activity from a representative channel that is applied to the signals of the other 5 channels.

#### *2.4. Windowing Technique 2.4. Windowing Technique*

time.

The EMG data segmentation is one of the important processes used to improve the performance and response time of EMG based pattern recognition control strategy and in this study, overlapping window technique introduced by [13] was adopted to segment the EMG signal into different analysis window. This technique is associated with windowing parameters (length and window increment), where a part of the new analysis window data overlaps with the current window data, and all analysis windows increase with processing time as shown in Figure 3. The EMG data segmentation is one of the important processes used to improve the performance and response time of EMG based pattern recognition control strategy and in this study, overlapping window technique introduced by [13] was adopted to segment the EMG signal into different analysis window. This technique is associated with windowing parameters (length and window increment), where a part of the new analysis window data overlaps with the current window data, and all analysis windows increase with processing time as shown in Figure 3.

**Figure 3.** The framework of the overlapping windowing technique for the extraction of feature sets. Note: WL denotes window length, Winc denotes window increment and PT represent the processing **Figure 3.** The framework of the overlapping windowing technique for the extraction of feature sets. Note: W<sup>L</sup> denotes window length, Winc denotes window increment and P<sup>T</sup> represent the processing time.

The processing time is the time require to extract feature sets and the classification algorithm to decode the motion intent. It is worth noting that the window increment is usually shorter than the The processing time is the time require to extract feature sets and the classification algorithm to decode the motion intent. It is worth noting that the window increment is usually shorter than the window length, ideally it is equivalent to the processing time [14]. According to Englehart and

classification performance reduces with shorter window length but with a faster controller response.

Hudgins [13], a longer window length allows more features to be extracted, resulting in higher classification accuracy, but results in a slower response time of the prosthetic controller, while classification performance reduces with shorter window length but with a faster controller response.

Considering the fact that the utilized windowing parameters would influence the extracted feature characteristics, it is important to determine the optimal windowing parameters that will result in the extraction of accurately robust feature set for multi-class limb movement intent decoding [14,17]. Although, different combinations of windowing parameters have been reported in previous studies with little or no justification in the selection of these parameters. Hence, overlapping analysis window technique with combination of window lengths raging between 100 ms~300 ms and increments from 50 ms–125 ms were examined in this study as shown in Table 1.


**Table 1.** The combinations of window lengths and increments considered.
