Wearable Sensor-Based Gait Analysis and Robotic Exoskeleton Control for Parkinson’s Patients †
Abstract
1. Introduction
1.1. Physiological Metrics
1.2. Kinetic Metrics
1.3. Kinematic Metrics
2. Materials and Methods
2.1. EMG Signal Acquisition
2.1.1. Preamplifier
2.1.2. Driven Right Leg (DRL) Circuit
2.1.3. Motion Artifact Filter
2.1.4. Anti-Aliasing Filter
2.1.5. Full-Wave Precision Rectifier
2.1.6. Moving Window Integration
2.2. Ground Reaction Force Acquisition
2.3. Inertial Measurement
2.4. Analog-to-Digital Module
3. Results
3.1. Physiological Measurements
3.2. Ground Reaction Force Measurements
3.3. Inertial Measurements
4. Discussion
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Brozova, H.; Stochl, J.; Roth, J.; Ruzicka, E. Fear of Falling Has Greater Influence than Other Aspects of Gait Disorders on Quality of Life in Patients with Parkinson’s Disease. Neuro Endocrinol. Lett. 2009, 30, 453–457. [Google Scholar]
- Caldas, R.; Mundt, M.; Potthast, W.; Buarque de Lima Neto, F.; Markert, B. A Systematic Review of Gait Analysis Methods Based on Inertial Sensors and Adaptive Algorithms. Gait Posture 2017, 57, 204–210. [Google Scholar] [CrossRef]
- Le, A.M.; Neuschatz, J.S.; Golding, J.M.; Jenkins, B.D.; Cutler, B.L. A Step Too Far: The Problems with Forensic Gait Analysis. In Advances in Psychology and Law; Springer: Berlin/Heidelberg, Germany, 2022; pp. 89–109. [Google Scholar] [CrossRef]
- Balakrishnan, A.; Medikonda, J.; Namboothiri, P.K.; Natarajan, M. Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson’s Disease Assessment: A Review. Eng. Sci. 2022, 19, 5–19. [Google Scholar] [CrossRef]
- Marinou, G.; Kourouma, I.; Mombaur, K. Development and Validation of a Modular Sensor-Based System for Gait Analysis and Control in Lower-Limb Exoskeletons. arXiv 2024, arXiv:2409.01174. [Google Scholar]
- Pinheiro, C.; Figueiredo, J.; Magalhães, N.; Santos, C.P. Wearable Biofeedback Improves Human-Robot Compliance during Ankle-Foot Exoskeleton-Assisted Gait Training: A Pre-Post Controlled Study in Healthy Participants. Sensors 2020, 20, 5876. [Google Scholar] [CrossRef] [PubMed]
- Bijalwan, V.; Semwal, V.B.; Mandal, T.K. Fusion of Multi-Sensor Based Biomechanical Gait Analysis Using Vision and Wearable Sensor. IEEE Sens. J. 2021, 21, 14213–14220. [Google Scholar] [CrossRef]
- Haque, M.R.; Imtiaz, M.H.; Kwak, S.T.; Sazonov, E.; Chang, Y.-H.; Shen, X. A Lightweight Exoskeleton-Based Portable Gait Data Collection System. Sensors 2021, 21, 781. [Google Scholar] [CrossRef]
- Sarajchi, M.; Sirlantzis, K. Pediatric Robotic Lower-Limb Exoskeleton: An Innovative Design and Kinematic Analysis. IEEE access 2023, 11, 115219–115230. [Google Scholar] [CrossRef]
- Guerra, B.M.V.; Schmid, M.; Sozzi, S.; Pizzocaro, S.; De Nunzio, A.M.; Ramat, S. A Recurrent Deep Network for Gait Phase Identification from EMG Signals during Exoskeleton-Assisted Walking. Sensors 2024, 24, 6666. [Google Scholar] [CrossRef]
- Chowdhury, R.; Reaz, M.; Ali, M.; Bakar, A.; Chellappan, K.; Chang, T. Surface Electromyography Signal Processing and Classification Techniques. Sensors 2013, 13, 12431–12466. [Google Scholar] [CrossRef] [PubMed]
- Kaur, M.; Mathur, S.; Bhatia, D.; Verma, S. EMG Analysis for Identifying Walking Patterns in Healthy Males. In Proceedings of the 2015 11th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME), Glasgow, UK, 29 June–2 July 2015. [Google Scholar] [CrossRef]
- Salman, A.; Iqbal, J.; Izhar, U.; Khan, U.S.; Rashid, N. Optimized Circuit for EMG Signal Processing. In Proceedings of the 2012 International Conference of Robotics and Artificial Intelligence, Rawalpindi, Pakistan, 22–23 October 2012. [Google Scholar] [CrossRef]
- Fleischer, C.; Wege, A.; Kondak, K.; Hommel, G. Application of EMG Signals for Controlling Exoskeleton Robots. Biomed. Technik. Biomed. Eng. 2006, 51, 314–319. [Google Scholar] [CrossRef] [PubMed]
- Tahir, A.M.; Chowdhury, M.E.H.; Khandakar, A.; Al-Hamouz, S.; Abdalla, M.; Awadallah, S.; Reaz, M.B.I.; Al-Emadi, N. A Systematic Approach to the Design and Characterization of a Smart Insole for Detecting Vertical Ground Reaction Force (VGRF) in Gait Analysis. Sensors 2020, 20, 957. [Google Scholar] [CrossRef] [PubMed]
- Kloeckner, J.; Visscher, R.M.S.; Taylor, W.R.; Viehweger, E.; De Pieri, E. Prediction of Ground Reaction Forces and Moments during Walking in Children with Cerebral Palsy. Front. Hum. Neurosci. 2023, 17, 1127613. [Google Scholar] [CrossRef] [PubMed]
- Seel, T.; Raisch, J.; Schauer, T. IMU-Based Joint Angle Measurement for Gait Analysis. Sensors 2014, 14, 6891–6909. [Google Scholar] [CrossRef]
- Htun, Z.M.M.; Latt, M.M.; New, C.M.; Mon, S.S.Y. Performance Comparison of Experimental-Based Kalman Filter and Complementary Filter for IMU Sensor Fusion by Applying Quadrature Encoder. Int. J. Sci. Res. Publ. (IJSRP) 2018, 8, 11. [Google Scholar] [CrossRef]
- Sardjono, T.A.; Kusuma, H.; Tasripan; Sugiarto, K. Comparative SNR Analysis between Instrument ADAS1000 and AD620. J. Sistim Inf. Teknol. 2022, 4, 129–133. [Google Scholar] [CrossRef]
- Ganesan, Y.; Gobee, S.; Durairajah, V. Development of an Upper Limb Exoskeleton for Rehabilitation with Feedback from EMG and IMU Sensor. Procedia Comput. Sci. 2015, 76, 53–59. [Google Scholar] [CrossRef]
- Gohel, V.; Mehendale, N. Review on Electromyography Signal Acquisition and Processing. Biophys. Rev. 2020, 12, 1361–1367. [Google Scholar] [CrossRef] [PubMed]
- Guo, C.; Wang, S.; Chai, Y.; Guo, Z. Design of Variable Gain Amplifier Circuit Based on Newton Rings Stress. In Proceedings of the International Conference on Chemical, Material and Food Engineering, Advances in Engineering Research/Advances in Engineering Research, Kunming, China, 25–26 July 2015. [Google Scholar] [CrossRef]
- Reaz, M.B.I.; Hussain, M.S.; Mohd-Yasin, F. Techniques of EMG Signal Analysis: Detection, Processing, Classification and Applications. Biol. Proced. Online 2006, 8, 11–35. [Google Scholar] [CrossRef]
- Levin, M. Safe Current Limits for Electromedical Equipment and Hazards to Patients. Circulation 1994, 90, 2160–2162. [Google Scholar] [CrossRef]
- Guerrero, F.N.; Spinelli, E. High Gain Driven Right Leg Circuit for Dry Electrode Systems. Med. Eng. Phys. 2017, 39, 117–122. [Google Scholar] [CrossRef]
- Boyer, M.; Bouyer, L.; Roy, J.-S.; Campeau-Lecours, A. Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review. Sensors 2023, 23, 2927. [Google Scholar] [CrossRef] [PubMed]
- Komi, P.V.; Tesch, P. EMG Frequency Spectrum, Muscle Structure, and Fatigue during Dynamic Contractions in Man. Eur. J. Appl. Physiol. Occup. Physiol. 1979, 42, 41–50. [Google Scholar] [CrossRef]
- Oyebola Blessed Olalekan Sallen-Key Topology, MFB and Butterworthy in Bandpass Design for Audio Circuit Design. Asian J. Manag. Sci. 2017, 6, 23–28. [CrossRef]
- Chi, Y.M.; Deiss, S.R.; Cauwenberghs, G. Non-Contact Low Power EEG/ECG Electrode for High Density Wearable Biopotential Sensor Networks. In Proceedings of the Wearable and Implantable Body Sensor Networks, Berkeley, CA, USA, 3–5 June 2009. [Google Scholar] [CrossRef]
- Baker, B.C. Designing an Anti-Aliasing Filter for ADCs in the Frequency Domain. Ind. Analog. Appl. J. 2015, 7, AAJ 2Q. [Google Scholar]
- Pandey, V.K.; Krishnan, N.; Pandey, P.C. Tracking Based Baseline Restoration for Acquisition of Impedance Cardiogram and Other Biosignals. In Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, 17–18 January 2006. [Google Scholar] [CrossRef]
- Aydoğan, İ.; Aydin, E.A. Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces. Politek. Derg. 2023, 26, 973–981. [Google Scholar] [CrossRef]
- Norizan, M.A.; Ali, F.; Abas, N.; Jamaluddin, H.; Nor, A.S.M. Electromyography Circuit Based on Forearm Muscle. J. Theor. Appl. Inf. Technol. 2015, 81, 331–336. [Google Scholar]
- Farfán, F.D.; Politti, J.C.; Felice, C.J. Evaluation of EMG Processing Techniques Using Information Theory. BioMed. Eng. OnLine 2010, 9, 72. [Google Scholar] [CrossRef]
- Ding, Q.; Xiong, A.; Zhao, X.; Han, J. A Novel EMG-Driven State Space Model for the Estimation of Continuous Joint Movements. In Proceedings of the 2011 IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA, 9–12 October 2011. [Google Scholar] [CrossRef]
- Muller, I.; de Brito, R.; Pereira, C.; Brusamarello, V. Load Cells in Force Sensing Analysis—Theory and a Novel Application. IEEE Instrum. Meas. Mag. 2010, 13, 15–19. [Google Scholar] [CrossRef]
- Pirani, H.; Azizi, M. Comparison of Peak Pressure, Maximum Force, Contact Area, and Contact Time between the Right and Left Foot in Elite Weightlifters. J. Kermanshah Univ. Med. Sci. 2020, 24, e96967. [Google Scholar] [CrossRef]
- Fitriani, D.A.; Andhyka, W.; Risqiwati, D. Design of Monitoring System Step Walking with MPU6050 Sensor Based Android. JOINCS J. Inform. Netw. Comput. Sci. 2017, 1, 1–8. [Google Scholar] [CrossRef]
- Higgins, W.T. A Comparison of Complementary and Kalman Filtering. IEEE Trans. Aerosp. Electron. Syst. 1975, AES-11, 321–325. [Google Scholar] [CrossRef]
- Cheng, P.L.; Nicol, A.; Paul, J.P. Determination of Axial Rotation Angles of Limb Segments—A New Method. J. Biomech. 2000, 33, 837–843. [Google Scholar] [CrossRef] [PubMed]
- dos Santos, D.A.; de Souza Soares, A.M.; Tupinambá, W.L.M. Development of a Portable Data Acquisition System for Extensometry. Exp. Tech. 2021, 46, 723–730. [Google Scholar] [CrossRef]
- Liu, C.; Alif, M.A.; He, G. Shoulder Motion Detection Algorithm Based on MPU6050 Sensor and XGBoost Model. In Proceedings of the 2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT), Xiamen, China, 5–7 August 2022. [Google Scholar] [CrossRef]
Specification | Stage 1 | Stage 2 | Equivalent |
---|---|---|---|
Passband (−3 dB) | 14 Hz | 20 Hz | 24 Hz |
Stopband (−40 dB) | 1 Hz | 1.5 Hz | 4 Hz |
Group Delay @fc | 10 ms | 8 ms | 18 ms |
C1 = C2 | 1 uF | 1 uF | - |
R1 | 16 kΩ%5 | 12 kΩ%5 | - |
R2 | 12 kΩ%5 | 9.1 kΩ%5 | - |
Specification | SPS = 860 | SPS = 240 | SPS = 128 |
---|---|---|---|
NoiseMAX (Value) | 19.05 | 11.54 | 5.50 |
NoiseMAX (Bits) | 4.25 | 3.53 | 2.45 |
Standart Deviation | 8.07 | 4.63 | 1.97 |
Specification | Control | Firmware Filter | No PGA (adj.) |
---|---|---|---|
NoiseMAX (Value) | 19.05 | 20.19 | 5.90 (adj. 23.6) |
NoiseMAX (Bits) | 4.25 | 4.33 | 2.56 (adj. 4.56) |
Standard Deviation | 8.07 | 8.28 | 2.02 (adj. 8.09) |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Bülbül, E. Wearable Sensor-Based Gait Analysis and Robotic Exoskeleton Control for Parkinson’s Patients. Eng. Proc. 2024, 82, 83. https://doi.org/10.3390/ecsa-11-20456
Bülbül E. Wearable Sensor-Based Gait Analysis and Robotic Exoskeleton Control for Parkinson’s Patients. Engineering Proceedings. 2024; 82(1):83. https://doi.org/10.3390/ecsa-11-20456
Chicago/Turabian StyleBülbül, Eren. 2024. "Wearable Sensor-Based Gait Analysis and Robotic Exoskeleton Control for Parkinson’s Patients" Engineering Proceedings 82, no. 1: 83. https://doi.org/10.3390/ecsa-11-20456
APA StyleBülbül, E. (2024). Wearable Sensor-Based Gait Analysis and Robotic Exoskeleton Control for Parkinson’s Patients. Engineering Proceedings, 82(1), 83. https://doi.org/10.3390/ecsa-11-20456