MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion
Abstract
:1. Introduction
2. Related Works
3. Materials and Methods
3.1. System Setup
3.2. Accelerometer Non-Orthogonal Error Estimation
3.3. Stance Phase Detection by Decision Level Data Fusion
3.4. Knee Angle Estimation
3.5. Attitude Estimation and Quaternion Correction in IMUs via Sensor Fusion
4. Experimental Results
4.1. Knee Flexion Monitoring
4.2. Feet Clearance Monitoring
5. Discussion and Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
MEMS | Micro-Electro-Mechanical Sensor |
BSN | Body Sensor Network |
IMU | Inertial Measurement Unit |
MARG | Magnetic Angular Rate and Gravity |
ZVU | Zero Velocity Updating |
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Gait Parameter | Description |
---|---|
Stride length (m) | Distance between two consecutive footprint of the same foot. |
Stride speed (m/s) | Stride length divided by walking cycle. |
Stride frequency | Number of steps taken per minute during walking. |
Walking cycle (s) | Duration of a single stride, inversely proportional to cadence. |
Stance time (s) | Duration of stance phase when feet contact with the ground, starting with initial-contact (IC) and ending with foot-off (FO) of the same foot. |
Swing time (s) | Duration of swing phase when feet swing above the ground, starting with FO and ending with IC. |
Clearance (m) | Foot elevation in swing phase, which reflects the muscular strength of lower limbs and can be diversified as maximum and minimum foot elevation. |
Plantar & dorsiflex (degrees) | The angle between the dorsum of the foot and the back of the leg. |
Knee ROM (degrees) | Range of knee flexion during a single stride. |
Items | Observation Method | Optical System | Inertial Body Sensor Network (BSN) |
---|---|---|---|
Objectivity | subjective | objective | objective |
Robustness | poor | sensitive to occlusion | very stable |
Repeatability | poor | high | high |
Efficiency | medium | low | high |
Set-up time | several minutes | half-hour | several minutes |
Usability | high | low | high |
Visual text | no | partial | fully |
Unit | Accelerometer | Gyroscope | Magnetometer |
---|---|---|---|
Dimensions | 3 axes | 3 axes | 3 axes |
Dynamic Range | m/s | deg/s | mGauss |
Bandwidth (Hz) | 30 | 40 | 10 |
Linearity (% of FS) | 0.2 | 0.1 | 0.2 |
Bias stability (unit ) | 0.02 | 1 | 0.1 |
Alignment Error (deg) | 0.1 | 0.1 | 0.1 |
Parameter | Healthy | Neurological | Arthropathy |
---|---|---|---|
Stride length (m) | 1.21 | 0.68 | |
Stride speed (m/s) | 0.94 | ||
Stride frequency | 92 | 64 | 72 |
Walking cycle (s) | 1.32 | 1.68 | 1.48 |
Stance time (s) | 0.86 | 1.14 | 0.99 |
Swing time (s) | 0.46 | 0.54 | 0.49 |
Clearance (m) | 0.22 | 0.08 | 0.14 |
Knee ROM (degrees) |
Joint Angle ° | Heel Strike | Foot Flat | Heel Off | Swing |
---|---|---|---|---|
Knee joint (Healthy subject) | ||||
Knee joint (Stroke Patient) | ||||
Ankle joint (Healthy subject) | ||||
Ankle joint (Stroke Patient) |
Item | Source | SS | df | MS | F | p-Value |
---|---|---|---|---|---|---|
Prior treatment | Columns | 285.1 | 19 | 15.0053 | 0.39 | 0.9787 |
Error | 778 | 20 | 38.9 | |||
Total | 1063.1 | 39 | ||||
Post treatment 2 weeks | Columns | 260.275 | 19 | 13.6987 | 1.26 | 0.3061 |
Error | 217.5 | 20 | 10.875 | |||
Total | 477.775 | 39 | ||||
Post treatment 6 weeks | Columns | 227.275 | 19 | 11.9618 | 3.39 | 0.0046 |
Error | 70.5 | 20 | 3.525 | |||
Total | 297.775 | 39 |
Item | Source | SS | df | MS | F | p-Value |
---|---|---|---|---|---|---|
Prior treatment | Columns | 304.28 | 19 | 16.0145 | 0.23 | 0.9988 |
Error | 1385.5 | 20 | 69.275 | |||
Total | 1689.78 | 39 | ||||
Post treatment 2 weeks | Columns | 203.9 | 19 | 10.7316 | 0.21 | 0.9993 |
Error | 1006 | 20 | 50.3 | |||
Total | 1209.9 | 39 | ||||
Post treatment 6 weeks | Columns | 165.6 | 19 | 8.7158 | 0.6 | 0.8637 |
Error | 290 | 20 | 14.5 | |||
Total | 455.6 | 39 |
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Share and Cite
Qiu, S.; Liu, L.; Zhao, H.; Wang, Z.; Jiang, Y. MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion. Micromachines 2018, 9, 442. https://doi.org/10.3390/mi9090442
Qiu S, Liu L, Zhao H, Wang Z, Jiang Y. MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion. Micromachines. 2018; 9(9):442. https://doi.org/10.3390/mi9090442
Chicago/Turabian StyleQiu, Sen, Long Liu, Hongyu Zhao, Zhelong Wang, and Yongmei Jiang. 2018. "MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion" Micromachines 9, no. 9: 442. https://doi.org/10.3390/mi9090442