Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review
Abstract
:1. Introduction
- To provide an overview of the current state-of-the-art research on estimation methods of (relative) 3D/2D positions of the human body, human body segments, or joints applying data fusion methods integrating UWB and MIMU sensor technology.
- To summarize the configurations, protocols, and algorithms used in these estimation methods.
- To summarize the achieved accuracy and stability reported for these methods.
- To discuss the strengths and limitations of these methods and their consequences in the context of future application of 3D analysis of human movement.
2. Methods
2.1. Search Procedure
- Group 1: UWB, ultra-wideband;
- Group 2: IMU, inertial measurement unit, IMMU, inertial magnetic measurement unit, MIMU, magnetic inertial measurement unit, inertial sensors, INS, inertial navigation system, inertial motion capture, accelerometer, gyroscope;
- Group 3: indoor positioning, position, indoor localization, localization, tracking, navigation, indoor navigation, trajectory tracking, distance, motion analysis, movement analysis, dynamic activity, ambulatory, posture, pose, orientation;
- Group 4: sensor fusion, data fusion, filtering algorithm, fusion, filter, Kalman filter, graph optimization, particle filter.
2.2. Study Selection
- The journal article was published within the last decade, that is between the years 2012 to 2023.
- The research used the integrated UWB and MIMU sensors alone for estimating positions.
- The research included experimental validation of the position estimation methods, which were validated against a standard reference system (vision-based systems such as VICON or other means where the ground truth is known).
- The sensor system was applied for human movement tracking, analysis of human movement, or human localization.
- The data fusion was based on UWB ranging or position estimates that used multiple sensor nodes instead of localization based on a single UWB sensor that uses the reflected signals similar to radar.
- They were not written in English.
- Additional sensors were used in data fusion along with the UWB/MIMU combination.
- They were conference proceedings, abstracts, review articles, or letters to editorial.
- They were applied or used for non-human situations such as drones, robots, etc.
2.3. Data Analysis
3. Results
3.1. Search Results
3.2. Literature Overview of Integrated UWB/MIMU Sensor System
3.2.1. UWB Sensor System Configuration and Measurement
3.2.2. MIMU Sensor System Configuration and Measurement
3.2.3. Placement Location of the Sensors on the Body
3.2.4. Position Estimation Methods Combining UWB/MIMU Data
3.2.5. Non-Line of Sight (NLOS) Mitigation Strategies
3.3. Accuracy and Stability of Position Estimates
4. Discussion
4.1. General
4.2. Position Estimation Accuracy and Stability
4.3. Effect on Position Estimation Accuracy Based on Sensor Configuration and Sensor Placement
4.4. Effect on Position Estimation Accuracy Based on the Data Fusion Algorithm/Methods
4.5. General Recommendations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
EFIR | Extended Finite Impulse Response |
EKF | Extended Kalman Filter |
FIR | Finite Impulse Response |
IH | Integrated Hardware |
KF | Kalman Filter |
LOS | Line of Sight |
MAE | Mean Absolute Error |
MAP | Maximum a Posteriori |
MIMU | Magnetic Inertial Measurement Unit |
MSE | Mean Square Error |
NIH | Non-integrated Hardware |
NLOS | Non-Line-of-Sight |
PDR | Pedestrian Dead Reckoning |
PF | Particle Filter |
RFID | Radio Frequency Identification |
RMSE | Root Means Square Error |
RSS | Received Signal Strength |
ToA | Time of Arrival |
TWR | Two-Way Ranging |
UFIR | Unbiased Finite Impulse Response |
UKF | Unscented Kalman Filter |
UWB | Ultra-wideband |
ZUPT | Zero Velocity Update algorithm |
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Ref. No. | Year | UWB Sensor | MIMU Sensor | UWB Config. | Update Rate [sps] | UWB Location | MIMU Location | UWB/MIMU Integration Method | UWB Update Param. | Sensor Attachment | |
---|---|---|---|---|---|---|---|---|---|---|---|
MIMU | UWB | ||||||||||
[37] | 2012 | - | - | 4 Anchor and 1 Tag | 200 | 3 to 4 | Held in hand | Attached to the subject’s ankles | Loosely coupled EKF | Position | Not rigidly attached |
[38] | 2015 | Ubisense Series 7000 | Xsens MTx | 4 Anchor and 1 Tag | 100 | 10 | Attached to the subject’s waist | Attached to the subject’s waist | Two-step cascaded KF | Position | Rigidly attached |
[39] | 2015 | Ubisense Series 7000 | Xsens MVN suit | 4 Anchor and 1 Tag | 100 | 16 | Attached to the subject’s waist | One unit on the waist and six units for the right and left thighs, shanks, and feet. | Loosely coupled two-step Cascaded KF | Position | Not rigidly attached |
[40] | 2015 | NA | Xsens | 10 Anchor and 1 Tag | 120 | 10 | Attached to the subject’s foot | Attached to the subject’s foot | Maximum a Posteriori (MAP) estimation algorithm | Distance | Rigidly attached |
[7] | 2017 | Unisense series 7000 | Xsens MVN suit | 4 Anchor and 3 Tag | 100 | 10 | Attached to the subject’s waist and both foot | One unit on the waist and six on the right and left thighs, shanks, and feet. | Multiple layered KF | Position | Rigidly attached |
[41] | 2017 | Unisense series 7000 | Xsens MTx | 4 Anchor and 1 Tag | 100 | 9.25 | Attached to the subject’s waist | One unit on the waist, and six on the right and left thigh, shanks, and feet | Cascaded KF | Position | Rigidly attached |
[42] | 2017 | - | - | 3 Anchor and 1 tag | - | - | Attached to the subject’s shoulder | Attached to both of the subject’s feet | Unscented Kalman Filter (UKF) | Distance | Not rigidly attached |
[32] | 2017 | DW1000 | - | 4 Anchor and 1 Tag | 128 | 2 | Attached to the helmet worn on head | Attached to the subject’s foot | PF | Position | Not rigidly attached |
[43] | 2018 | BeSpoon | MATE9 (Huawei smartphone) | 4 Anchor and 1 Tag | - | - | Held in hand | Held in hand | EKF | Distance | Rigidly attached |
[31] | 2018 | DWM1000 | X-IMU, UK MPU9250 | 4 Anchor and 1 Tag | 128 | 3 | Attached to the helmet worn on head | Attached to the subject’s foot | Method1: PF | Position | Not rigidly attached |
Method2: a combination of PF & EKF filter | Position | ||||||||||
[44] | 2018 | DWM1000 | X-IMU, UK MPU9250 | 4 Anchor and 1 Tag | 128 | 2 | Attached to the helmet worn on head | Attached to the subject’s foot | Graph optimization | Position | Not rigidly attached |
[45] | 2018 | - | - | 4 Anchor and 1 Tag | - | - | Attached to a backpack setup carried by the subject | Attached to the subject’s foot | Federated Extended Finite Impulse Response (EFIR) filter | Distance | Not rigidly attached |
[46] | 2018 | DW1000 | Acc:ADXL203;Gyr:ADXRS620;Mag:HMC5983 | 5 Anchor and 1 Tag | - | - | Attached to a backpack setup carried by the subject | Attached to the subject’s foot | UFIR filter bank | Position | Not rigidly attached |
[47] | 2019 | EVB1000/DW1000 | Iphone IMU | 1 Anchor and 1 Tag | 50 | 10 | Held in hand | Held in hand | PF | Distance | Rigidly attached |
[48] | 2019 | - | - | 4 Anchor and 1 Tag | 100 | - | Attached to a backpack setup carried by the subject | Attached to the subject’s foot | Predictive Adaptive Kalman Filter (PAKF) | Distance | Not rigidly attached |
[49] | 2019 | - | Acc:ADXL203;Gyr:ADXRS620;Mag:HMC5983 | 5 Anchor and 1 Tag | - | - | Attached to a backpack setup carried by the subject | Attached to the subject’s foot | Predictive UFIR filter | Position | Not rigidly attached |
[50] | 2019 | DW1000 | iPhone IMU | 3 Anchor and 1 Tag | 50 | Held in hand | Held in hand | PF | Distance | Rigidly attached | |
[51] | 2019 | DW1000 | LSM330DLC;Mag:HMC5883L | 3 Anchor and 1 Tag | 100 | 1 to 2 | - | - | EKF | Position | - |
[52] | 2019 | - | JY901B | 4 Anchor and 1 Tag | - | 1 | Held in hand | - | EKF | Position | - |
[53] | 2019 | - | MPU9150 | 7 Anchor and 1 Tag | 100 | 1 | Attached to the helmet worn on head | Attached to the subject’s foot | Iterative EKF | Distance | Not rigidly attached |
[33] | 2020 | DWM1000 | MPU9250 | 3 Anchor and 1 Tag | - | - | Attached to the subject’s foot | Attached to the subject’s foot | KF | position | Rigidly and integrated hardware |
[54] | 2020 | UWB Mini4Plus | JY61 attitude sensor | 4 Anchor and 1 Tag | - | - | Attached to the subject’s foot | Attached to the subject’s foot | EKF | position | Rigidly attached |
[55] | 2020 | - | - | 4 Anchor and 1 Tag | 100 | - | Attached to a backpack setup carried by the subject | Attached to the subject’s foot | Decision Tree-EFIR filter | Distance | Not rigidly attached |
[56] | 2020 | - | - | 4 Anchor and 1 Tag | - | - | Attached to a backpack setup carried by the subject | Attached to the subject’s foot | Least Square-Support Vector Machine (LS- SVM) assisted UFIR filter | Position | Not rigidly attached |
[57] | 2020 | EVB1000/DW1000 | iPhone 7 IMU | 3 Anchor and 1 Tag | 50 | 3.57 | Held in hand | Held in hand | PF | Distance | Rigidly attached |
[58] | 2020 | EVB1000/DW1000 | iPhone 7 IMU | 2 Anchor and 1 Tag | 50 | 3.57 | Held in hand | Held in hand | PF | Distance | Rigidly attached |
[57] | 2020 | EVB1000/DW1000 | iPhone 7 IMU | 2 Anchor and 1 Tag | 50 | 3.57 | Held in hand | Held in hand | PF | Distance | Rigidly attached |
[35] | 2020 | DWM1000/MAX2000 | LIS3DH | 3 Anchor and 1 Tag | - | - | - | - | EKF | Distance | - |
1 Anchor and 1 tag | UKF | - | |||||||||
[34] | 2020 | PlusOn410 | Starneto, China | 5 Anchor and 1 Tag | 100 | 10 | Attached to the subject’s foot | Attached to the subject’s foot | KF | Position | Rigidly attached |
[59] | 2020 | DWM1000 | Acc. & Gyr.:ICM20602;Mag: IST8310 | 3 Anchor and 1 Tag | 200 | 20 | Attached to the subject’s shoulder | Attached to the subject’s shoulder | KF | Distance | Rigidly and integrated hardware |
[60] | 2021 | DW1000 | Acc:ADXL203;Gyr:ADXRS620;Mag:HMC5983 | 5 Anchor and 1 Tag | 30 | 3 | Attached to a backpack setup carried by the subject | Attached to the subject’s foot | EFIR filter | Distance | Not rigidly attached |
[61] | 2021 | MAX2000/DW1000 | MPU6050 | 4 Anchor and 1 Tag | - | - | Attached to the subject’s foot | Attached to the subject’s foot | tightly-coupled EKF | Distance | Rigidly and integrated hardware |
[62] | 2021 | DW1000 | EBIMU-9DOF | 4 Anchor and 1 Tag | 50 | 16 | Held in hand at a constant location close to the chest | Held in hand at a constant location close to the chest | KF | Position | Rigidly attached |
[63] | 2021 | - | MPU9250 | 5 Anchor and 1 Tag | 100 | 2 | Attached to the subject’s foot | Attached to the subject’s foot | EKF | Distance | Rigidly attached |
[64] | 2021 | - | Acc:ADXL203;Gyr:ADXRS620;Mag:HMC5983 | 4 Anchor and 1 Tag | - | - | Attached to a backpack setup carried by the subject | Attached to the subject’s foot | Distributed KF | Distance | Not rigidly attached |
[65] | 2022 | DWM1001C | MPU6050 InvenSense, San Jose, CA, USA | 4 Anchor and 1 Tag | 100 | 10 | Attached to the subject’s foot | Attached to the subject’s foot | loosely coupled EKF | Position | Rigidly attached |
[66] | 2023 | DW1000 | - | 4 Anchor and 1 Tag | - | - | Body worn; On-body location not mentioned | Body worn; On-body location not mentioned | KF | Position | - |
Ref. No. | 2D/3D | LOS/NLOS | Exp. Activities | Integrated Accuracy [m] | Errors Around Each Axis [m] | Test Time [s] | |||
---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | Min Error | Max Error | ||||||
[37] | 2D | Combined LOS and NLOS | Walking | - | 0.4 | - | - | - | ~240 |
[38] | 2D | LOS | Jumping task | - | <0.04 | - | - | <0.04 in x and y axis | 90 |
[39] | 3D | LOS | Walking and Jumping | Walking: 0.068 * Jumping: 0.073 * | - | - | - | Walking: X-0.039 ± 0.016; Y-0.036 ± 0.015; Z-0.043 ± 0.017 Jumping: X-0.042 ± 0.019; Y-0.036 ± 0.015; Z-0.049 ± 0.023 | ~120 |
[40] | 3D | LOS | Walking | 0.048 * | - | - | - | X-0.03; Y-0.03; Z-0.023 | 24 |
[7] | 3D | LOS | Walking and Jumping | Waist: 0.075 * Feet: 0.067 * | - | - | - | Waist: X-0.043; Y-0.048; Z-0.038; Feet: X-0.039; Y-0.041; Z-0.035 | 100 |
[41] | 3D | LOS | Walking and Dynamic (combining Running and Jumping) | Overall: 0.108 Walking: 0.092 Dynamic: 0.129 | - | - | - | Overall: X-0.074; Y-0.072; Z-0.030; Walking: X-0.063; Y-0.062; Z-0.026; Dynamic: X-0.086; Y-0.086; Z-0.04 | 75 |
[42] | 2D | LOS | Walking at different speeds from 1–3 m/s | - | Overall: 0.15 Speed 1 m/s: 0.129 Speed 2 m/s: 0.155 Speed 3 m/s: 0.195 | 0.05 | 0.35 | - | 30 |
[32] | 2D | Separate LOS and NLOS | Walking | - | LOS: 0.708 ± 0.660 NLOS: 0.726 ± 0.661 | - | - | ~24 to 58 | |
[43] | 2D | Combined LOS and NLOS | Walking along two different routes | Route 1 (Less NLOS): 0.35 Route 2 (More NLOS): 0.45 | - | - | - | - | - |
[31] | 2D | Separate LOS and NLOS | Walking along two different routes | - | LOS Route 1: 0.637 Route 2: 0.531 NLOS Route 1: 0.735 Route 2: 0.571 | LOS Route 1: 0.001 Route 2: 0.001 NLOS Route 1: 0.003 Route 2: 0.007 | LOS Route 1: 2.087 Route 2: 1.462 NLOS Route 1: 2.896 Route 2: 1.816 | - | ~390 to 420 |
- | LOS Route 1: 0.685 Route 2: 0.505 NLOS Route 1: 0.624 Route 2: 0.527 | LOS Route 1: 0.003 Route 2: 0.009 NLOS Route 1: 0.003 Route 2: 0.008 | LOS Route 1: 2.576 Route 2: 1.356 NLOS Route 1: 2.576 Route 2: 1.524 | - | |||||
[44] | 2D | Combined LOS and NLOS | Walking along three different routes | - | Route 1: 0.413 Route 2: 0.369 Route 3: 0.372 | - | - | - | ~100 to 180 |
[45] | 2D | LOS | Walking | 0.576 * | - | - | - | X-0.36; Y-0.45 | 45 |
[46] | 2D | LOS | Walking | 0.297 * | - | - | X-0.2; Y-0.22 | 60 | |
[47] | 2D | LOS | Walking along two different routes | - | Route 1: 0.60 Route 2: 0.58 | - | - | - | 258.8 & 391.8 |
[48] | 2D | LOS | Walking | 0.299 * | - | - | - | X-0.173 **; Y-0.245 ** | 30 |
[49] | 2D | LOS | Walking along three different routes | Route 1: 0.391 * Route 2: 0.353 * Route 3: 0.700 * | - | - | Route 1: X-1.03; Y-1.21 Route 2: X-0.44; Y-1.20 Route 3: X-2.28; Y-2.20 | Route 1: X-0.25; Y-0.30 Route 2: X-0.15; Y-0.32 Route 3: X-0.50; Y-0.49 | 25 |
[50] | 2D | Combined LOS and NLOS | Walking along two different routes | - | Route 1: 0.125 ± 0.059 Route 2: 0.164 ± 0.084 | - | - | - | 50 & 100 |
[51] | 2D | Separate LOS and NLOS | Walking | - | LOS: less than 1.5 m for 99th percentile; NLOS: Less than 2 m for the 99th percentile | - | - | - | - |
[52] | 2D | Combined LOS and NLOS | NA | - | ~0.4 | 0.05 | - | - | - |
[53] | 2D | Combined LOS and NLOS | Walking along two different routes | - | Route 1: 7-anchor: 0.58 ± 0.22 3-Anchor: 0.62 ± 0.33 2-anchor: 0.63 ± 0.34 Route 2: 7-anchor: 0.59 ± 0.27 3-Anchor: 0.66 ± 0.32 2-anchor: 0.96 ± 0.47 | - | NA | 180 & 585 | |
[33] | 2D | LOS | Walking | - | 0.076 | - | - | X-0.051; Y-0.055 | ~20 to 25 |
[54] | 2D | Separate LOS and NLOS | Walking | - | LOS: 0.24 ± 0.26 NLOS: 0.35 ± 0.35 | - | LOS: 1.52 NLOS: 1.02 | LOS: X-0.30; Y-0.18 NLOS: X-0.43; Y-0.23 | ~20 to 40 |
[55] | 2D | LOS | Walking | 0.788 * | - | - | - | X-0.364; Y-0.699 | ~20 to 25 |
[56] | 2D | LOS | Walking | 0.264 * | - | - | - | X-0.173 **; Y-0.200 ** | ~270 |
[57] | 2D | Combined LOS and NLOS | Walking along two different routes | - | Route 1: 0.87 ± 0.52 Route 2: 0.81 ± 0.39 | - | - | - | 323 & 447 |
[58] | 2D | Combined LOS and NLOS | Walking | - | 2.09 ± 1.33 | - | - | - | ~479 |
[57] | 2D | LOS | Walking | - | 2.48 | - | - | - | 334.1 & 329.9 |
[35] | 2D | LOS | NA | < 0.2 | - | - | - | - | NA |
<0.16 | - | - | - | - | |||||
[34] | 2D | LOS | Walking | 0.132 | - | - | - | - | ~550 |
[59] | 2D | Combined LOS and NLOS | Walking | - | 0.157 | - | 0.601 | - | ~50 |
[60] | 2D | LOS | Walking | 0.305 * | - | - | - | X-0.20; Y-0.23 | 90 |
[61] | 3D | LOS | Walking | - | 50% of the time below 0.39 | - | - | - | 325 |
[62] | 2D | Combined LOS and NLOS | Walking along six different routes | Less NLOS Route 1: 0.234; Route 2: 0.39; Route 3: 0.556 More NLOS Route 4: 0.314; Route 5: 0.492; Route 6: 0.473 Average: 0.4266 | - | - | - | - | ~15 to 30 |
[63] | 2D | Combined LOS and NLOS | Walking along two different routes | - | Route 1: 0.48 ± 0.37 Route 2: 0.62 Reduced no of Anchors: 4Anc: 0.36 ± 0.24; 3Anc: 0.51 ± 0.24 (Best combi) | - | - | - | 250 |
[64] | 2D | LOS | Walking along two different routes | Route 1: 0.61 Route 2: 0.53 | - | - | - | - | 460 |
[65] | 2D | Combined LOS and NLOS | Walking along two different routes | - | Route 1: 0.24 Route 2: 0.29 | - | Route 1: 0.47 Route 2: 0.66 | Route 1: X-0.18; Y-0.15 Route 2: X-0.18; Y-0.24 | ~10 to 20 |
[66] | 2D | Separate LOS and NLOS | Walking along two different routes | NLOS: 0.128 LOS: 0.099 | - | - | - | - | ~5 to 10 |
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Yogesh, V.; Buurke, J.H.; Veltink, P.H.; Baten, C.T.M. Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review. Sensors 2023, 23, 7277. https://doi.org/10.3390/s23167277
Yogesh V, Buurke JH, Veltink PH, Baten CTM. Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review. Sensors. 2023; 23(16):7277. https://doi.org/10.3390/s23167277
Chicago/Turabian StyleYogesh, Vinish, Jaap H. Buurke, Peter H. Veltink, and Chris T. M. Baten. 2023. "Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review" Sensors 23, no. 16: 7277. https://doi.org/10.3390/s23167277