A Critical Review and Systematic Design Approach for Linkage-Based Gait Rehabilitation Devices
Abstract
:1. Introduction
2. Current Gait Training Devices
2.1. Exoskeleton
2.2. End-Effector
2.3. Mobile Powered
2.4. Ankle Joint Assist
2.5. Cable-Driven
3. Search Methodology
4. Linkage-Based Devices
- Simplicity and low volume;
- Kinematic accuracy compared to human natural gait;
- Maximization of sensory inputs;
- Adaptability to different subjects.
4.1. Planar Four-Bar Linkages
4.2. Planar Five-Bar Linkages and Six Bar-Linkages
4.3. Planar Higher-Order Linkages and Spatial Linkages
4.4. Critical Analysis and Discussion
5. Key Points towards the Design of New Linkage-Based Devices
- References to serve as gait baselines, preferably from a database or normative data;
- Topology selection criteria and the kinematic synthesis process;
- Measurement systems and accuracy characteristics used to obtain experimental data;
- Kinematic accuracy indicator(s) aligned with the literature and clinical gait analysis;
- Compatibility with the Bernstein principle “repetition without repetition”;
- Possibility of gradual training towards a final kinematic trajectory;
- Target population based on anthropometrical lengths and sizes, grouped.
6. A Possible New Design of a Linkage-Based Gait Trainer
6.1. Gait Baselines
6.2. Linkage Topology Choice, Synthesis, and Optimization
6.3. Prototype and Testing
6.4. Final Remarks
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Device | Type 1 | Joints | Range of Motion (ROM) | Planes of Motion | Body Weight Support | Gait Surface 2 | Max Speed |
---|---|---|---|---|---|---|---|
Lokomat [20,27] | E | Torso Hip Knee Ankle | ±4 cm/±4° Adjustable Adjustable Passive | Sagittal Frontal Transverse | Harness | TM | 3.2 km/h |
Robogait [39,40] | E | Hip Knee Ankle | Adjustable Adjustable Passive | Sagittal | Harness | TM | 3.2 km/h |
Ekso [33,41] | M | Hip Knee Ankle | +135°/−20° +130°/0° +10°/−10° | Sagittal Transverse | Crutch Cain Walker | OG | 3.5 km/h |
HAL [42,43] | M | Hip Knee | 120°/−20° 120°/−6° | Sagittal | Not included | FS (indoor) | Not specified |
Gait Trainer GT I [44] | EE | CoM (vert/horiz.) Ankle | 1 cm/2 cm Adjustable (gears) | Sagittal | Harness | FPs | 140 steps/min |
Gait Trainer GT II [44,45] | EE | Ankle | Step length: 34–48 cm | Sagittal | Harness | FPs | 2 km/h |
Haptic Walker [46] | EE | Ankle | Adjustable | Sagittal | Harness | FPs | 5 km/h |
G-EO System [47,48] | EE | Ankle | Step length: max 55 cm Step height: 10–20 cm | Sagittal | Harness | FPs | 2.3 km/h |
Anklebot [35,36] | AJA | Ankle | DF/PF: 25°/45° IV/EV: 25°/20° IR/ER: 15°/15° | All | Not included | TM OG | Not specified |
C-ALEX [49,50,51] | CD | Hip Knee | +43.8° ± 7.4°/−11.6° ± 1.8° 84.3° ± 7.6°/0° | Sagittal | Not included | TM | 1.6 km/h 3 |
ROPES [37,52] | CD | Hip Knee Ankle | Adjustable Adjustable Adjustable | Sagittal | Harness | TM | 5.4 km/h 3 |
CaLT [53] | CD | Knee | Adjustable | Sagittal | Harness | TM | 5.4 km/h 3 |
Four-Bar Linkage | Synthesis/Optimization | Accuracy Measure | Gait Baseline |
---|---|---|---|
[62] | Multi-objective | RMS error and Peak error 1 | Dataset [66] |
[63] | Constrained nonlinear multivariable | RMS error | Normative (scaled) |
[64] | Nonlinear least squares | Sum of error distances | Biomechanical model |
[65] | Shape optimization + topology optimization | Error bound | Dataset [66] |
Linkage Topology | Synthesis/Optimization | Accuracy Measure | Gait Baseline |
---|---|---|---|
Five-bar [67] | Kinematic mapping and rigid body guidance | Position Error Angle Error | Experimental |
Five-bar [68] | Rigid body guidance (not explicit) | Not mentioned | Experimental |
Six-bar [69,70,71] | Unconstrained optimization | Error Function | Experimental |
Six-bar [72] | Combined dual particle swarm optimization | Coupler vs. reference: Average distance Maximum distance Sum of distances | Experimental |
Six-bar [73] | Deep generative models (conditional–variational auto-encoders) | RMSE 1 for gait prediction models | Individual–specific, using gait prediction models trained on a real dataset (KIST dataset) |
Six-bar [75] | Deep generative neural network | MSE 2 | Normative [76] |
Linkage Topology | Synthesis/Optimization | Accuracy Measure | Gait Baseline |
---|---|---|---|
Seven-bar [77] | Genetic algorithm | Average residuals | Dataset [78] |
Eight-bar [79] | Global optimization (MultiStart) | Hip and knee angles: Mean Standard deviation | Dataset [82,83] |
Eight-bar [80] | Interior point method | RMSE | Dataset [66] |
Spatial linkage [81] | Bio-inspired | Not verified | Not applicable |
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Paiva, T.S.d.; Gonçalves, R.S.; Carbone, G. A Critical Review and Systematic Design Approach for Linkage-Based Gait Rehabilitation Devices. Robotics 2024, 13, 11. https://doi.org/10.3390/robotics13010011
Paiva TSd, Gonçalves RS, Carbone G. A Critical Review and Systematic Design Approach for Linkage-Based Gait Rehabilitation Devices. Robotics. 2024; 13(1):11. https://doi.org/10.3390/robotics13010011
Chicago/Turabian StylePaiva, Thiago Sá de, Rogério Sales Gonçalves, and Giuseppe Carbone. 2024. "A Critical Review and Systematic Design Approach for Linkage-Based Gait Rehabilitation Devices" Robotics 13, no. 1: 11. https://doi.org/10.3390/robotics13010011