# Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. Materials and Methods

#### 3.1. Data Collection

^{−5}g, the attitude measuring stability was 0.01°, and the baud rate range was 2400–921,600 bps. The baud rate used in the experiment was 115,200 bps and the sampling frequency was 50 Hz.

#### 3.2. Data Preprocessing

#### 3.3. Gait Phase Division

#### 3.4. Proposed LSTM-DNN Algorithm

#### 3.5. Evaluation Methods

## 4. Experimental Results

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Acceleration data collected under the three body parts: foot (

**a**), calf (

**b**), and thigh (

**c**). a_x represents the acceleration in the x-axis direction, a_y represents the acceleration in the y-axis direction, a_z represents the acceleration in the z-axis direction, a_com represents the combined acceleration.

**Figure 3.**Schematic diagram of the gait phase division. a_x represents the acceleration in the x-axis direction, a_y represents the acceleration in the y-axis direction, a_z represents the acceleration in the z-axis direction.

**Figure 4.**Sliding window segmentation of acceleration data. Here RF represents the acquired foot acceleration data, RC represents the acquired calf acceleration data, and RT represents the collected thigh acceleration data.

**Table 1.**Acceleration data for different parts at different speeds using a principal components analysis (PCA) synthesized parameter table.

Pace | Collection Location | z_1 | z_2 | z_3 |
---|---|---|---|---|

0.78 m/s | Calf | 0.658 | 0.659 | 0.365 |

Thigh | −0.556 | 0.569 | 0.365 | |

Foot | 0.645 | 0.499 | 0.579 | |

1.0 m/s | Calf | 0.636 | 0.645 | 0.423 |

Thigh | −0.479 | 0.597 | 0.643 | |

Foot | 0.667 | 0.545 | 0.508 | |

1.25 m/s | Calf | 0.639 | 0.631 | 0.440 |

Thigh | −0.565 | 0.533 | 0.630 | |

Foot | 0.703 | 0.566 | 0.431 |

Kernel Function | Pace | Accuracy | Precision | Recall | F-score |
---|---|---|---|---|---|

Linear | 0.78 m/s | 0.590 | 0.680 | 0.600 | 0.638 |

1.00 m/s | 0.660 | 0.710 | 0.670 | 0.689 | |

1.25 m/s | 0.720 | 0.720 | 0.710 | 0.715 | |

rbf | 0.78 m/s | 0.770 | 0.780 | 0.780 | 0.780 |

1.00 m/s | 0.710 | 0.770 | 0.690 | 0.728 | |

1.25 m/s | 0.750 | 0.780 | 0.740 | 0.759 | |

poly | 0.78 m/s | 0.490 | 0.250 | 0.500 | 0.333 |

1.00 m/s | 0.500 | 0.250 | 0.500 | 0.333 | |

1.25 m/s | 0.610 | 0.610 | 0.610 | 0.610 | |

sigmoid | 0.78 m/s | 0.500 | 0.250 | 0.500 | 0.333 |

1.00 m/s | 0.610 | 0.640 | 0.600 | 0.650 | |

1.25 m/s | 0.630 | 0.630 | 0.630 | 0.630 |

K | Pace | Accuracy | Precision | Recall | F-score |
---|---|---|---|---|---|

2 | 0.78 m/s | 0.690 | 0.740 | 0.670 | 0.703 |

1.00 m/s | 0.620 | 0.700 | 0.630 | 0.663 | |

1.25 m/s | 0.730 | 0.760 | 0.740 | 0.750 | |

5 | 0.78 m/s | 0.760 | 0.780 | 0.760 | 0.770 |

1.00 m/s | 0.690 | 0.700 | 0.700 | 0.700 | |

1.25 m/s | 0.720 | 0.730 | 0.730 | 0.730 | |

7 | 0.78 m/s | 0.700 | 0.740 | 0.700 | 0.719 |

1.00 m/s | 0.680 | 0.710 | 0.680 | 0.695 | |

1.25 m/s | 0.640 | 0.660 | 0.630 | 0.645 | |

10 | 0.78 m/s | 0.670 | 0.730 | 0.660 | 0.693 |

1.00 m/s | 0.650 | 0.660 | 0.640 | 0.619 | |

1.25 m/s | 0.680 | 0.690 | 0.680 | 0.685 | |

15 | 0.78 m/s | 0.670 | 0.690 | 0.670 | 0.680 |

1.00 m/s | 0.670 | 0.680 | 0.670 | 0.675 | |

1.25 m/s | 0.570 | 0.690 | 0.620 | 0.653 | |

20 | 0.78 m/s | 0.640 | 0.680 | 0.620 | 0.649 |

1.00 m/s | 0.600 | 0.620 | 0.590 | 0.605 | |

1.25 m/s | 0.620 | 0.650 | 0.630 | 0.640 | |

30 | 0.78 m/s | 0.610 | 0.670 | 0.610 | 0.639 |

1.00 m/s | 0.620 | 0.660 | 0.630 | 0.645 | |

1.25 m/s | 0.520 | 0.550 | 0.510 | 0.529 |

Algorithm | Accuracy | Precision | Recall | F-score |
---|---|---|---|---|

LSTM-DNN | 0.951 | 0.962 | 0.941 | 0.951 |

LSTM | 0.919 | 0.939 | 0.901 | 0.920 |

KNN | 0.760 | 0.780 | 0.760 | 0.770 |

SVM | 0.770 | 0.780 | 0.780 | 0.780 |

Algorithm | Accuracy | Precision | Recall | F-score |
---|---|---|---|---|

LSTM-DNN | 0.918 | 0.936 | 0.905 | 0.920 |

LSTM | 0.868 | 0.903 | 0.828 | 0.864 |

KNN | 0.690 | 0.700 | 0.700 | 0.700 |

SVM | 0.710 | 0.770 | 0.690 | 0.728 |

Algorithm | Accuracy | Precision | Recall | F-score |
---|---|---|---|---|

LSTM-DNN | 0.924 | 0.913 | 0.946 | 0.950 |

LSTM | 0.886 | 0.891 | 0.881 | 0.932 |

KNN | 0.730 | 0.760 | 0.740 | 0.750 |

SVM | 0.750 | 0.780 | 0.740 | 0.759 |

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**MDPI and ACS Style**

Zhen, T.; Yan, L.; Yuan, P.
Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm. *Algorithms* **2019**, *12*, 253.
https://doi.org/10.3390/a12120253

**AMA Style**

Zhen T, Yan L, Yuan P.
Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm. *Algorithms*. 2019; 12(12):253.
https://doi.org/10.3390/a12120253

**Chicago/Turabian Style**

Zhen, Tao, Lei Yan, and Peng Yuan.
2019. "Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm" *Algorithms* 12, no. 12: 253.
https://doi.org/10.3390/a12120253