# Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Participants

#### 2.2. sEMG Recording Setup and Observational Experiments

#### 2.3. Data Preparation

#### 2.3.1. Signal Preprocessing

- A fourth-order Butterworth bandpass filter (BPF) ranging from 20 to 300 Hz;
- Hampel filtering for artefact reduction by identifying outliers deviating from an average of more than double the standard deviation in the neighboring 100 samples;
- Root-mean-square signal envelope and sEMG data normalization.

- The maximum peak variable likelihood estimation was set to 8, cutting off the first and last gestures from each 10-gesture repeated set.
- Minimal temporal distances between the peaks were configured as 0.1 s.
- To avoid false variables, the estimation of peak selection was set at 0.25 percentile of the difference from neighboring signal peaks.
- For temporal standardization, signal boundaries (including the manually predefined ‘rest’ label) were determined by the window length of 30 ms for the inferior limit and 60 ms for the superior limit with regard to the peak.

#### 2.3.2. Feature Extraction

#### 2.3.3. Feature Validation and Visualization

#### 2.3.4. Feature Vector Dimensional Reduction

#### 2.4. Machine Learning and Classification Algorithms

#### 2.5. Statistical Evaluation

_{1}-score (F

_{1}) which were derived from the confusing matrix output values of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) [59]. Overall, these performance metrics indicate whether the given gesture was correctly classified or mislabeled and explore the specific ratio of prediction potential, as indicated below.

_{1}-score, as a harmonic mean of precision (positive predictive value) and recall (true positive rate), demonstrates the summarized accuracy of the method which is used in bioelectrical signal processing:

_{1}in pairs amid classifiers for the non-affected side and affected side to reveal tendencies in prediction between the identic and different algorithms. The same procedures were performed for the PCA-dependent performance using certain best principal components (PCs) of each classifier to study dimensional reduction and statistical significance. For this testing, the best PC values were derived from the best harmonic mean scores.

## 3. Results

#### 3.1. Patient Characteristics

#### 3.2. Accuracy Rates and Confusion Matrices of Paretic and Non-Affected Extremities

#### 3.3. PCA Dimensional Impact on Supervised Model Performance

^{®}Core™ i9-7900X 3.30 Hz, 128 GB RAM, 1 TB SSD). The dimensional impact on the CM charts is shown in Figures S8–S13.

_{1}-score, in general, those metrics were correlated with accuracy, turned out to be sensitive in dimensional investigation, and had larger deviation parameters across GLs.

_{1}-scores.

#### 3.4. Classifier Statistical Evaluation and Comparison Using Dimensional Shift

_{1}(SVM without and LDA with the PCA) were evaluated to validate pairs of highest interest for statistical validity (Tables S1 and S2). Among max performance scores, the p-value remained less than 0.05 for most of the pairs, except the GL7 model in the paretic dataset, where significance for both Acc and F

_{1}was not observed without the use of specific PCA settings.

#### 3.5. Summary of Hand Gesture Prediction Rates

_{1}was correlated throughout gesture model complexity variations of all classifiers (Table 5), where the harmonic mean turned out to be sensitive due to a wide gradation of paresis and limited number of studied patients (with lower scores for ‘fist’ and higher for ‘rest’).

_{1}remained stable for the non-paretic side but decreased for the paretic side starting from GL5. Contrary to SVM without using PCA, following the processing of dimensional, reduced feature vectors, performance dropped several percent in general and flipped the accuracy trendline, whereby GL4 had higher scores and GL7 the lowest.

_{1}also increased for most of the gestures in each GL. As a consequence, the use of PCA elevated LDA performance and allowed for the outperformance of SVM for most myoelectric pattern recognition scenarios.

_{1}between paretic and non-paretic sides were the lowest (Figure 8). Dimensional reduction did not have a strong effect on the accuracy of k-NN while the F

_{1}for each gesture model drastically decreased.

_{1}, as well as performance stability, compared to LDA and k-NN for both extremities. LDA had a reduced performance compared to SVM for both paretic and non-affected extremities without additional dimensional settings (Table 5). Lastly, k-NN was able to preserve sufficient accuracy scores for the paretic and non-paretic sides with and without PCA. On the other hand, by having only a fractional feature vector for classification using k-NN, the dimensional shift did not change the single-gesture class prediction in GLs.

## 4. Discussion

## 5. Conclusions

## 6. Patents

## Supplementary Materials

_{1}-score performance metrics obtained by with and without the use of PCA.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Representative four-channel sEMG data labeling and segmentation. FCR: flexor carpi radialis; FCU: flexor carpi ulnaris; APB: abductor pollicis brevis (thenar area); EDC: extensor digitorum communis.

**Figure 3.**The diagram of the ASF-4 wavelet transformation and additional feature extraction into ASF-14NP and ASF-24P feature sets. The pale-red shape shows the key part of the feature sets, while the blue shapes demonstrate feature concatenation to enhance vector weight.

**Figure 4.**The 3D mesh illustrates the relationship between GLs in paretic and non-paretic datasets (left figures obtained from NP19, right from P19), number of PCs, and accuracy: (

**a**,

**b**) SVM, (

**c**,

**d**) LDA, and (

**e**,

**f**) k-NN performance. The color heatmap mesh represents the accuracy result range from dark blue (lower scores) to yellow (higher scores).

**Figure 5.**Total mean accuracy rate among GLs using SVM, LDA, and k-NN. Asterisks indicates statistical significance between paretic and non-paretic pairs using a paired t-test (* p < 0.05).

**Figure 6.**Total mean accuracy rates of GLs from four to seven using SVM, LDA, and k-NN, dependent on the application of PCA (* p < 0.05) on paretic (P19) and non-paretic (NP19) datasets: PCA-dependent GL4 (

**a**), GL5 (

**b**), GL6 (

**c**), and GL7 (

**d**) model. Legend options: dark blue (NP19) and blue (P19) bars are the Acc from SVM, red (NP19) and pale-red (P19) bars are the Acc from LDA, dark-green (NP19) and green (P19) bars are the Acc from k-NN. Asterisks between the paretic and non-paretic evaluations of the single classifier indicate statistical significance (* p < 0.05).

**Figure 7.**Testing difference between classifiers (SVM, LDA, and k-NN) using a paired t-test among paretic and non-paretic sides with and without the use of PCA: right top is GL4, left top is GL5, left bottom is GL6, and right bottom is GL7 model. The dark-blue squares indicate a significance between the assessed pairs of classifiers (p < 0.05) and black color shows non-significance (p > 0.05).

**Figure 8.**Total accuracy rate and F

_{1}-score from GL models on NP19 and P19 datasets without the PCA: top figures show mean Acc and F

_{1}from SVM, middle figures show mean Acc and F

_{1}from LDA, and bottom figures show mean Acc and F

_{1}from k-NN.

**Figure 9.**Total accuracy rate and F

_{1}-score from GLs models on NP19 and P19 with best PCA settings: top figures show mean Acc and F

_{1}from SVM, middle figures show mean Acc and F

_{1}from LDA, and bottom figures show mean Acc and F

_{1}from k-NN.

**Figure 10.**The relationship between the number of gestures and the average value of the F

_{1}-score (cross-validated by SVM with and without PCA for paretic and non-paretic datasets): (

**a**,

**c**) NP19 without and with the use of PCA; (

**b**,

**d**) P19 with and without the use of PCA.

Domain | Feature | Internal Parameters | Short Description |
---|---|---|---|

Time domain | TM4-5 | $m=4,5$ | $T{M}_{m}=\left|\frac{1}{N}{\displaystyle \sum}_{n-1}^{N}{x}_{n}^{m}\right|,$ [27]. |

LCARD | Threshold set to 0.001 | LCARD examines the number of unique values in the time-series set among each channel [55]. | |

Time–frequency domain | HHT | HHT is a high-order signal processing model of empirical mode decomposition and the Hilbert transform [53,54]. | |

MEWP | Wavelet Daubechies (Db4) | MEWP provides enriched signal analysis by wavelet decomposition set of parameters: position, decomposed signal scaling, and frequency curve [43,56]. |

Domain | Feature | Internal Parameters | Short Description |
---|---|---|---|

Time domain | LRMSV2-3 | $\gamma =2,3$ | $\mathrm{LRMSV}\gamma =log{\left(\frac{1}{N}{\displaystyle \sum}_{n=1}^{N}{x}_{n}^{\gamma}\right)}^{\frac{1}{\gamma}}$; |

ASM | $exp=\left(\right)open="\{">\begin{array}{c}0.5,if\left(n\ge 0.25Nandn\le 0.75\right)\\ 0.75,otherwise\end{array}$; | $\mathrm{ASM}=\left|\frac{{{\displaystyle \sum}}_{n=1}^{N}{\left({x}_{n}\right)}^{e}}{N}\right|$; | |

ASR | $\mathrm{ASR}=\left|{\displaystyle \sum}_{n=1}^{N}{\left({x}_{n}\right)}^{\frac{1}{2}}\right|$; | ||

AAC | $\mathrm{AAC}=\frac{1}{N}{\displaystyle \sum}_{n=1}^{N-1}\left|{x}_{n+1}-{x}_{n}\right|$; | ||

HPC | $Activity=\frac{1}{N}{\displaystyle \sum}_{n=0}^{N-1}{\left({x}_{n}-\overline{x}\right)}^{2}$$,\text{}Mobility=\sqrt{\frac{Activity\left(dn/dt\right)}{Activity}}$; | $\mathrm{HPC}=\sqrt{\frac{Mobility\left(dn\u2215dt\right)}{Mobility}}$; | |

Frequency domain | MMDF | $\mathrm{MMDF}=\frac{1}{2}{\displaystyle \sum}_{j=1}^{M}{A}_{j}$; | |

SMD | $\mathrm{SMD}=\frac{1}{2}{\displaystyle \sum}_{n=1}^{M}PS{D}_{n}$; | ||

Spatial domain | FER-4 | The normalized mean value of the ratio between flexors and extensors channels. |

Domain | Feature | Internal Parameters | Short Description |
---|---|---|---|

Time domain | MMAV2,MMAV5 | ${w}_{n}=\left(\right)open="\{">\begin{array}{c}1,0.25N\le n\le 0.75\mathrm{N}\\ \frac{4n}{N},n0.25N\\ \frac{4\left(N-n\right)}{N},otherwise\end{array}$; ${w}_{n}=\left(\right)open="\{">\begin{array}{c}\frac{4n}{N},n0.25\mathrm{N}\\ \frac{4n}{N}-1,0.25N\le n\le 0.5\mathrm{N}\\ \frac{4n}{N}-2,0.5N\le n\le 0.75\mathrm{N}\\ \frac{4n}{N}-3,otherwise\end{array}$; | $\mathrm{MMAV}2=\frac{1}{N}{\displaystyle \sum}_{n=1}^{N}{w}_{n}\left|{x}_{n}\right|$; $\mathrm{MMAV}5\text{}\mathrm{splits}\text{}\mathrm{the}\text{}\mathrm{signal}\text{\u2019}\mathrm{s}\text{}\mathrm{energy}\text{}\mathrm{window}\text{}({w}_{n}$) of interest aiming to investigate the 3/5th of 4/5th window segment. Equitation is similar to MMAV2 [25,57]. |

SSI | $\mathrm{SSI}={\displaystyle \sum}_{n=1}^{N}{\left|{x}_{n}\right|}^{2}$; | ||

KURT | $\mathrm{KT}=\frac{N\left(N+1\right)}{\left(N-1\right)\left(N-2\right)\left(N-3\right)}{\displaystyle \sum}_{n=1}^{N}{\left({x}_{n}-\overline{x}\right)}^{4}-3\frac{{\left(N-1\right)}^{2}}{\left(N-2\right)\left(N-3\right)}$; | ||

SD | $\mathrm{SD}=\sqrt{\frac{1}{N}{\displaystyle \sum}_{n=1}{\left({x}_{n}-\overline{x}\right)}^{2}}$; | ||

MFL | $\mathrm{MFL}={log}_{10}\left(\sqrt{{\displaystyle \sum}_{n=1}^{N}{\left({x}_{n}-{x}_{n+1}\right)}^{2}}\right)$; | ||

WL | Threshold set to 0.05 | $\mathrm{WL}={\displaystyle \sum}_{n=1}^{N-1}\left|{x}_{n+1}-{x}_{n}\right|$; | |

MHW | $\mathrm{MHW}={\displaystyle \sum}_{n=0}^{N}{\left({W}_{n}{x}_{n}\right)}^{2}$; | ||

AR3 | ${x}_{n}={a}_{0}+a{r}_{1}\left({x}_{n}-1\right)+a{r}_{2}\left({x}_{n}-2\right)+a{r}_{3}\left({x}_{n}-3\right)$; | $\mathrm{AR}3=\left[{a}_{0},a{r}_{1},a{r}_{2},a{r}_{3}\right]$, order set to 3 | |

LPC3 | ${x}_{n}={b}_{0}+{b}_{1}{x}_{n}+{b}_{2}({x}_{n}-1)+{b}_{3}\left({x}_{n}-2\right)$; | $\mathrm{LPC}3=\left[{b}_{0},{b}_{1},{b}_{2},{b}_{3}\right]$, order set to 3 | |

Frequency domain | MASP | $\mathrm{Fast}\text{}\mathrm{Fourier}\text{}\mathrm{transform}\text{}\mathrm{parameters}\text{}\mathrm{are}\text{}\mathrm{split}\text{}\mathrm{into}\text{}5\text{}\mathrm{bins}\text{}ff{t}_{k}\left(k=1,2,3,4,5\right)$ | $\mathrm{MASP}={\displaystyle \sum}_{n=1}^{N}\frac{\left|ff{t}_{k}\right|}{N}$; |

SMN | $\mathrm{SMN}=\frac{{{\displaystyle \sum}}_{j=1}^{M}{f}_{j}PS{D}_{j}}{{{\displaystyle \sum}}_{j=1}^{M}PS{D}_{j}}$; | ||

MMNF | $\mathrm{MMNF}=\frac{{{\displaystyle \sum}}_{j=1}^{M}{f}_{j}{A}_{j}}{{{\displaystyle \sum}}_{j=1}^{M}{A}_{j}}$; | ||

Time–frequency domain | STFT | $\mathrm{SFTT}={\displaystyle \sum}_{r=1}^{N-1}x\left(r\right)g\left(r-n\right){\u03f5}^{-j2\pi mi/N}$; | |

EWT | Wavelet Daubechies (Db4) | $\mathrm{EWT}=\sqrt{\frac{1}{K}{\displaystyle \sum}_{k=1}^{K}{W}_{J,k}^{2}}$; | |

STW | STW is a noise-resilience method of wavelet transform and STFT to highlight signal window length other than artefacts or defect stochastic window frames [60]. | ||

Fractal domain | HFD | HFD evaluates muscle strength and the contraction grade; it measures the size and complexity of the sEMG signal in the time-domain spectrum without fractal attractor reconstruction methods [58]. |

Patient | Age, Gender | Lesion | Days Since Onset | BS | SIAS | FMA-UE | MAS | Affected Side |
---|---|---|---|---|---|---|---|---|

HGR-001 | 80, M | CI | 11 | 5, 5 | 4, 4 | 58 | 0, 0, 0 | R |

HGR-002 | 32, F | CI | 11 | 5, 5 | 4, 4 | 50 | 0, 0, 0 | R |

HGR-003 | 71, M | ICH | 13 | 5, 5 | 4, 4 | 64 | 1, 0, 0 | R |

HGR-004 | 52, F | CI | 8 | 6, 6 | 5, 5 | 63 | 0, 0, 0 | L |

HGR-005 | 82, M | CI | 9 | 4, 3 | 3, 1 | 27 | 1, 0, 0 | L |

HGR-006 | 81, M | ICH | 5 | 2, 4 | 1, 1 | 16 | 0, 0, 0 | R |

HGR-007 | 77, M | CI | 9 | 6, 6 | 5, 5 | 60 | 0, 0, 0 | R |

HGR-008 | 79, F | ICH | 7 | 3, 4 | 2, 3 | 28 | 0, 1+, 1+ | R |

HGR-009 | 65, M | ICH | 5 | 6, 6 | 5, 5 | 55 | 0, 0, 0 | R |

HGR-010 | 67, F | ICH | 12 | 5, 4 | 3, 1 | 37 | 0, 0, 0 | L |

HGR-011 | 66, M | CI | 13 | 3, 3 | 2, 1 | 15 | 1+, 0, 0 | L |

HGR-012 | 64, F | ICH | 33 | 4, 5 | 3, 4 | 35 | 1, 1, 0 | L |

HGR-013 | 63, M | CI | 13 | 5, 5 | 4, 4 | 59 | 1, 0, 0 | R |

HGR-014 | 50, M | CI | 19 | 3, 3 | 2, 1 | 22 | 1, 1, 0 | R |

HGR-015 | 72, F | CI | 16 | 6, 5 | 5, 4 | 52 | 0, 0, 0 | R |

HGR-016 | 57, M | ICH | 12 | 5, 4 | 4, 4 | 42 | 0, 0, 0 | L |

HGR-017 | 57, M | ICH | 18 | 2, 2 | 1, 0 | 8 | 0, 0, 0 | L |

HGR-018 | 64, M | CI | 9 | 6, 6 | 4, 4 | 60 | 0, 0, 0 | R |

HGR-019 | 74, F | CI | 12 | 2, 1 | 1, 0 | 9 | 0, 1, 0 | L |

**Table 5.**Mean average value of accuracy and F

_{1}scores of supervised gesture recognition from four to seven gesture labels (based on the hand movements of 19 stroke patients) without and with PCA.

Gesture Classification without PCA (GL4, GL5, GL6, GL7) | Gesture Classification with PCA * (GL4, GL5, GL6, GL7) | |||||||
---|---|---|---|---|---|---|---|---|

Non-Paretic Side (NP19) | Paretic Side (P19) | Non-Paretic Side (NP19) | Paretic Side (P19) | |||||

Classifier | Acc (%) | F_{1}(%) | Acc (%) | F_{1}(%) | Acc (%) | F_{1}(%) | Acc (%) | F_{1}(%) |

SVM | 94.80 | 89.74 | 88.71 | 77.53 | 92.81 | 85.63 | 87.59 | 74.90 |

94.19 | 85.69 | 88.48 | 71.49 | 91.41 | 78.77 | 86.07 | 65.16 | |

94.13 | 82.67 | 88.60 | 66.01 | 91.36 | 74.33 | 85.98 | 57.82 | |

94.73 | 81.82 | 89.75 | 64.26 | 91.97 | 72.00 | 86.40 | 52.20 | |

LDA | 87.63 | 75.42 | 78.16 | 55.48 | 94.05 | 88.13 | 88.64 | 77.15 |

90.92 | 77.52 | 77.09 | 41.42 | 93.79 | 84.55 | 87.96 | 69.94 | |

92.35 | 77.39 | 77.39 | 30.63 | 93.85 | 81.66 | 87.97 | 63.98 | |

93.27 | 76.95 | 77.43 | 20.09 | 94.02 | 79.25 | 88.17 | 58.61 | |

k-NN | 92.84 | 85.82 | 87.32 | 74.73 | 86.30 | 72.58 | 82.64 | 65.36 |

91.98 | 80.10 | 87.38 | 68.67 | 87.12 | 68.03 | 83.26 | 58.37 | |

91.70 | 75.34 | 86.81 | 60.72 | 87.70 | 63.76 | 84.24 | 53.03 | |

91.71 | 71.12 | 87.31 | 55.86 | 88.19 | 59.61 | 85.23 | 48.49 |

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Anastasiev, A.; Kadone, H.; Marushima, A.; Watanabe, H.; Zaboronok, A.; Watanabe, S.; Matsumura, A.; Suzuki, K.; Matsumaru, Y.; Ishikawa, E.
Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides. *Sensors* **2022**, *22*, 8733.
https://doi.org/10.3390/s22228733

**AMA Style**

Anastasiev A, Kadone H, Marushima A, Watanabe H, Zaboronok A, Watanabe S, Matsumura A, Suzuki K, Matsumaru Y, Ishikawa E.
Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides. *Sensors*. 2022; 22(22):8733.
https://doi.org/10.3390/s22228733

**Chicago/Turabian Style**

Anastasiev, Alexey, Hideki Kadone, Aiki Marushima, Hiroki Watanabe, Alexander Zaboronok, Shinya Watanabe, Akira Matsumura, Kenji Suzuki, Yuji Matsumaru, and Eiichi Ishikawa.
2022. "Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides" *Sensors* 22, no. 22: 8733.
https://doi.org/10.3390/s22228733