# Inertial Sensors as a Tool for Diagnosing Discopathy Lumbosacral Pathologic Gait: A Preliminary Research

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## Abstract

**:**

## 1. Introduction

- What are the different values of knee angle during a normal and pathological gait in four cases measured by IMU?
- Are there statistical differences of the knee’s angles in a healthy person, people with discopathy, and a person after surgical treatment?
- Could wavelet analysis (WA) be used to observe the asymmetry of a gait?

## 2. Materials and Methods

- prepare the patient for testing (interview about the illnesses locomotor) and connect the gateway to the computer and sensor parameter settings,
- mount the sensor as shown in Figure 1, turn sensors on and verify that data is received from all of them,
- standing starting position for the examination of the patient,
- resetting the button resets the algorithm used for calculating the orientation. After resetting, it is necessary to hold the nodes still for a few seconds to stabilize their orientation;
- start recording (patient goes) and stop recording,
- collect all the raw data from all sensors to a central computer via the USB (by using the wireless system at the highest possible data rate there is packet loss). Therefore, packet loss can be avoided by enabling flash logging and downloading the logs after the experiment (via USB cable is faster),
- process the data on the computer and mark the beginning of each of the left and right step and calculation of the knee angles for the left and right leg as the arithmetic mean of several steps,
- data transformation into gait cycle and plots,
- statistical analysis,
- wavelet analysis.

## 3. Cases Description

## 4. Results

#### 4.1. Inertial Measurement Unit

#### 4.2. Statistical Analysis

- Null Hypothesis H0: The curves have a normal distribution.
- Alternative Hypothesis H1: The curves do not have a normal distribution.

#### 4.3. The Gait Analysis Wavelet Concept

_{X}, a

_{Y}, a

_{Z}), related to the three directions of the Cartesian coordinate system of each sensor. The sum of the components is taken into consideration as the analyzed signal, which describes the movement of the part of a leg. The signal includes acceleration of gravity. Because the human gait is characterized by periodicities and simultaneously some characteristic periods may occur in specific time periods, the wavelet tool is chosen. The advantage afforded by wavelets is the ability to perform a local analysis. The wavelets are localized in time and scale, wavelet coefficients are able to localize characteristic changes or differences in analyzed signals [18]. By shifting parameters of wavelets, they can be applied as a focus directed to the interesting signal area described by time and scale related to frequencies. A detailed methodology of this procedure in this area was presented in [5,10]. The example of wavelet analysis of three steps during a healthy gait person for sensors number 2 and 5 placed on the right and left leg is shown in Figure 5a–d. It is the analyzed signal and the signal after reconstruction by using inverse wavelet transform (WT) [16]. These analyses are conducted in the case of normal gait i.e., (nonaffected side). In the upper row of figures, one can see at first measured and next reconstructed signals (red curves), respectively for A, B, C, and D leg.

## 5. Discussion

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**The human model and coordinate systems. The X, Y, Z coordinates represent the global coordinate system, the x

_{s}, y

_{s}, z

_{s}represents the sensor coordinate system (

**a**), subject wearing the ProMove mini nodes (

**b**).

**Figure 3.**Sagittal plane magnetic resonance imaging (MRI) cases: A—case (

**a**) B—case (

**b**) C—case (

**c**) D—case (before surgery) (

**d**).

**Figure 5.**Continuous wavelet transform of the analyzed signal obtained by using Morlet wavelet of parameter 4, where modulus and real parts of wavelet coefficients are shown in case of an A-health and B, C, and D-affected leg gait.

**Figure 6.**Continuous wavelet transform of the analyzed signal obtained by using Morlet wavelet of parameter 4, where modulus, real part of wavelet coefficients are shown in case of an A-health and B, C, and D-affected leg gait.

**Figure 7.**Continuous wavelet transform of the analyzed signal obtained by using Morlet wavelet of parameter 4, where pseudo-frequencies of wavelet coefficients are shown in case of A, B, C, and D patient health and affected side gait.

Case | Height [m] | Mass [kg] | BMI | Mean Step Length [m] (SD) | Mean Stride Frequency m [Hz] (SD) | Mean Velocity (m/s) (SD) |
---|---|---|---|---|---|---|

A | 1.76 | 74 | 23.62 | 0.83 (0.06) | 1.69 (0.13) | 1.40 (0.10) |

B | 1.77 | 77 | 24.58 | 0.75 (0.05) | 1.43 (0.16) | 1.07 (0.10) |

C | 1.75 | 72 | 23.51 | 0.73 (0.06) | 1.35 (0.13) | 0.98 (0.09) |

D | 1.77 | 70 | 22.34 | 0.77 (0.09) | 1.58 (0.16) | 1.06 (0.12) |

Case | Shapiro-Wilk | Lilliefors | Kołomogorov-Smirnov | Jarque-Bera | ||||
---|---|---|---|---|---|---|---|---|

Stat Value | p | Stat Value | p | Stat Value | p | Stat Value | p | |

Ah | 0.89 | p < 0.0001 | 0.17 | p < 0.01 | 0.17 | p < 0.01 | 12.07 | 0.0024 |

Bh | 0.88 | p < 0.0001 | 0.16 | p < 0.01 | 0.17 | p < 0.05 | 11.71 | 0.0029 |

Ch | 0.90 | p < 0.0001 | 0.11 | p < 0.01 | 0.17 | p < 0.20 | 9.84 | 0.0073 |

Dh | 0.87 | p < 0.0001 | 0.17 | p < 0.01 | 0.17 | p < 0.01 | 12.34 | 0.0021 |

Ah | 0.90 | p < 0.0001 | 0.16 | p < 0.01 | 0.16 | p < 0.01 | 10.78 | 0.0046 |

Ba | 0.87 | p < 0.0001 | 0.19 | p < 0.01 | 0.19 | p < 0.01 | 13.54 | 0.0011 |

Ca | 0.94 | 0.0003 | 0.08 | p < 0.20 | 0.08 | p > 0.20 | 5.66 | 0.0590 |

Da | 0.93 | 0.0001 | 0.11 | p < 0.01 | 0.11 | p > 0.20 | 6.55 | 0.0378 |

Affected Side | Health Side | ||||||||
---|---|---|---|---|---|---|---|---|---|

Ah | Ba | Ca | Da | Ah | Bh | Ch | Dh | ||

Affected | Ah | - | 0.9920 | 0.8005 | 0.8466 | 0.9813 | 0.9824 | 0.9202 | 0.9732 |

Ba | 0.9920 | - | 0.7403 | 0.7948 | 0.9939 | 0.9669 | 0.8800 | 0.9476 | |

Ca | 0.8005 | 0.7403 | - | 0.9890 | 0.7167 | 0.8384 | 0.9400 | 0.8834 | |

Da | 0.8466 | 0.7948 | 0.9890 | - | 0.7696 | 0.8852 | 0.9696 | 0.9245 | |

Health | Ah | 0.9813 | 0.9939 | 0.7167 | 0.7696 | - | 0.9415 | 0.8465 | 0.9204 |

Bh | 0.9824 | 0.9669 | 0.8384 | 0.8852 | 0.9415 | - | 0.9613 | 0.9920 | |

Ch | 0.9202 | 0.8800 | 0.9400 | 0.8465 | 0.8465 | 0.9613 | - | 0.9810 | |

Dh | 0.9732 | 0.9476 | 0.8834 | 0.9245 | 0.9204 | 0.9920 | 0.9810 | - |

Case | MeanSIG | Case | MeanSIG |
---|---|---|---|

Ah | 2.95 | Ah | 3.10 |

Bh | 2.43 | Ba | 2.23 |

Ch | 1.81 | Ca | 1.30 |

Dh | 2.30 | Da | 1.98 |

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**MDPI and ACS Style**

Glowinski, S.; Łosiński, K.; Kowiański, P.; Waśkow, M.; Bryndal, A.; Grochulska, A.
Inertial Sensors as a Tool for Diagnosing Discopathy Lumbosacral Pathologic Gait: A Preliminary Research. *Diagnostics* **2020**, *10*, 342.
https://doi.org/10.3390/diagnostics10060342

**AMA Style**

Glowinski S, Łosiński K, Kowiański P, Waśkow M, Bryndal A, Grochulska A.
Inertial Sensors as a Tool for Diagnosing Discopathy Lumbosacral Pathologic Gait: A Preliminary Research. *Diagnostics*. 2020; 10(6):342.
https://doi.org/10.3390/diagnostics10060342

**Chicago/Turabian Style**

Glowinski, Sebastian, Karol Łosiński, Przemysław Kowiański, Monika Waśkow, Aleksandra Bryndal, and Agnieszka Grochulska.
2020. "Inertial Sensors as a Tool for Diagnosing Discopathy Lumbosacral Pathologic Gait: A Preliminary Research" *Diagnostics* 10, no. 6: 342.
https://doi.org/10.3390/diagnostics10060342