Sensing and Control Strategies Used in FES Systems Aimed at Assistance and Rehabilitation of Foot Drop: A Systematic Literature Review
Abstract
:1. Introduction
1.1. Sensing Techniques
1.2. Control Strategies
1.3. Objectives and Structure of the Paper
2. Materials and Methods
2.1. Search Strategy
2.1.1. Types of Population
2.1.2. Types of Intervention
2.1.3. Types of Comparison
2.1.4. Types of Outcomes
2.2. Research Questions
2.3. Inclusion and Exclusion Criteria
2.4. Quality Assessment
2.5. Data Extraction and Analysis
3. Results
3.1. RQ1: What Types of Sensors Are Used to Measure the Body’s State and Its Response to FES?
- Accuracy
- Wearability
- Affordability
3.2. RQ2: What Variables Does the Sensor Measure in Gait-Oriented FES Systems?
3.3. RQ3: Which Control Strategies Are Employed in Lower Limb FES Systems and Applications?
3.4. RQ4: What Stimulation Parameters Are Modulated in CL Lower Limb FES Applications?
4. Discussion
4.1. Sensing
4.2. Control Strategies
- Applying a multimodal sensing strategy, comprising more than one sensor, is a highly recommended approach in CL gait-oriented FES systems for FD, with the potential to improve system accuracy and performance.
- Novel sensing and control strategies, such as US sensors and machine/deep learning-based controllers, should be considered, possibly combined with the multimodal approach mentioned above. To achieve this, fast and reliable digitizing and processing systems are required, to be feasible in real-life applications.
- The integration of markerless motion analysis tools, employing computer vision and depth sensors, is an alternative approach worthwhile to explore in gait-oriented FES applications, the relevance of which has already been shown in upper limb and stability training applications.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Pilkar, R.; Ramanujam, A.; Nolan, K.J. Alterations in Spectral Attributes of Surface Electromyograms after Utilization of a Foot Drop Stimulator during Post-Stroke Gait. Front. Neurol. 2017, 8, 449. [Google Scholar] [CrossRef] [PubMed]
- Peishun, C.; Haiwang, Z.; Taotao, L.; Hongli, G.; Yu, M.; Wanrong, Z. Changes in Gait Characteristics of Stroke Patients with Foot Drop after the Combination Treatment of Foot Drop Stimulator and Moving Treadmill Training. Neural Plast. 2021, 2021, 1–5. [Google Scholar] [CrossRef]
- Alouane, M.A.; Rifai, H.; Kim, K.; Amirat, Y.; Mohammed, S. Hybrid impedance control of a knee joint orthosis. Ind. Robot Int. J. Robot. Res. Appl. 2019, 46, 192–201. [Google Scholar] [CrossRef]
- Page, A.P.; Freeman, C.T. Point-to-point repetitive control of functional electrical stimulation for drop-foot. Control Eng. Pract. 2020, 96, 104280. [Google Scholar] [CrossRef]
- Nori, S.L.; Stretanski, M.F. Foot Drop. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2024. Available online: http://www.ncbi.nlm.nih.gov/books/NBK554393/ (accessed on 28 July 2024).
- Nath, R.K.; Somasundaram, C. Incidence, Etiology, Risk Factors Associated with Foot Drop. Eplasty 2023, 23, e16. [Google Scholar]
- Carvalho, S.; Correia, A.; Figueiredo, J.; Martins, J.M.; Santos, C.P. Functional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network. Machines 2021, 9, 253. [Google Scholar] [CrossRef]
- Meng, L.; Porr, B.; Gollee, H. Technical developments of functional electrical stimulation to restore gait functions: Sensing, control strategies, and current commercial systems. Chin. J. Sci. Instrum. 2017, 2017, 1319–1334. [Google Scholar]
- Khan, M.A.; Fares, H.; Ghayvat, H.; Brunner, I.C.; Puthusserypady, S.; Razavi, B.; Lansberg, M.; Poon, A.; Meador, K.J. A systematic review on functional electrical stimulation based rehabilitation systems for upper limb post-stroke recovery. Front. Neurol. 2023, 14, 1272992. [Google Scholar] [CrossRef] [PubMed]
- Schauer, T. Sensing motion and muscle activity for feedback control of functional electrical stimulation: Ten years of experience in Berlin. Annu. Rev. Control 2017, 44, 355–374. [Google Scholar] [CrossRef]
- Salchow-Hömmen, C.; Skrobot, M.; Jochner, M.C.E.; Schauer, T.; Kühn, A.A.; Wenger, N. Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Front. Hum. Neurosci. 2022, 16, 768575. [Google Scholar] [CrossRef]
- Seel, T.; Raisch, J.; Schauer, T. IMU-Based Joint Angle Measurement for Gait Analysis. Sensors 2014, 14, 6891–6909. [Google Scholar] [CrossRef]
- Strbac, M.; Malesevic, N.; Cobeljic, R.; Schwirtlich, L. Feedback control of the forearm movement of tetraplegic patient based on microsoft kinect and multi-pad electrodes. J. Autom. Control 2013, 21, 7–11. [Google Scholar] [CrossRef]
- Strbac, M.; Kočović, S.; Marković, M.; Popović, D.B. Microsoft kinect-based artificial perception system for control of functional electrical stimulation assisted grasping. BioMed Res. Int. 2014, 2014, 740469. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Q.; Lambeth, K.; Iyer, A.; Sun, Z.; Sharma, N. Ultrasound Imaging-Based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction. IEEE Trans. Control Syst. Technol. 2023, 31, 989–1005. [Google Scholar] [CrossRef]
- Gil-Castillo, J.; Alnajjar, F.; Koutsou, A.; Torricelli, D.; Moreno, J.C. Advances in neuroprosthetic management of foot drop: A review. J. Neuroeng. Rehabil. 2020, 17, 46. [Google Scholar] [CrossRef]
- Zahradka, N.; Behboodi, A.; Wright, H.; Bodt, B.; Lee, S. Evaluation of Gait Phase Detection Delay Compensation Strategies to Control a Gyroscope-Controlled Functional Electrical Stimulation System During Walking. Sensors 2019, 19, 2471. [Google Scholar] [CrossRef]
- Chaikho, L.; Clark, E.; Raison, M. Transcutaneous Functional Electrical Stimulation Controlled by a System of Sensors for the Lower Limbs: A Systematic Review. Sensors 2022, 22, 9812. [Google Scholar] [CrossRef] [PubMed]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
- Xu, R.; Jiang, N.; Mrachacz-Kersting, N.; Lin, C.; Prieto, G.A.; Moreno, J.C.; Pons, J.L.; Dremstrup, K.; Farina, D. A closed-loop brain-computer interface triggering an active ankle-foot orthosis for inducing cortical neural plasticity. IEEE Trans. Biomed. Eng. 2014, 61, 2092–2101. [Google Scholar] [CrossRef]
- Meng, L.; Porr, B.; Macleod, C.A.; Gollee, H. A functional electrical stimulation system for human walking inspired by reflexive control principles. Proc. Inst. Mech. Eng. H 2017, 231, 315–325. [Google Scholar] [CrossRef]
- de Melo, P.L.; da Silva, M.T.; Martins, J.; Newman, D. A microcontroller platform for the rapid prototyping of functional electrical stimulation-based gait neuroprostheses. Artif. Organs 2015, 39, E56–E66. [Google Scholar] [CrossRef]
- Alibeji, N.; Kirsch, N.; Dicianno, B.E.; Sharma, N. A Modified Dynamic Surface Controller for Delayed Neuromuscular Electrical Stimulation. IEEE ASME Trans. Mechatron. 2017, 22, 1755–1764. [Google Scholar] [CrossRef] [PubMed]
- Ahmed, M.; Huq, M.S.; Ibrahim, B.S.K.K. A New Method towards Achieving FES-Induced Movement. Int. J. Integr. Eng. 2018, 10, 30–34. [Google Scholar] [CrossRef]
- Le Guillou, R.; Schmoll, M.; Sijobert, B.; Borges, D.L.; Fachin-Martins, E.; Resende, H.; Pissard-Gibollet, R.; Fattal, C.; Coste, C.A. A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling. Sensors 2021, 21, 4571. [Google Scholar] [CrossRef]
- Coelho-Magalhães, T.; Coste, C.A.; Resende-Martins, H. A Novel Functional Electrical Stimulation-Induced Cycling Controller Using Reinforcement Learning to Optimize Online Muscle Activation Pattern. Sensors 2022, 22, 9126. [Google Scholar] [CrossRef]
- Braz, G.P.; Russold, M.F.; Fornusek, C.; Hamzaid, N.A.; Smith, R.M.; Davis, G.M. A novel motion sensor-driven control system for FES-assisted walking after spinal cord injury: A pilot study. Med. Eng. Phys. 2016, 38, 1223–1231. [Google Scholar] [CrossRef] [PubMed]
- Perumal, R.; Wexler, A.S.; Kesar, T.M.; Jancosko, A.; Laufer, Y.; Binder-Macleod, S.A. A phenomenological model that predicts forces generated when electrical stimulation is superimposed on submaximal volitional contractions. J. Appl. Physiol. 2010, 108, 1595–1604. [Google Scholar] [CrossRef]
- Hobbs, B.; Artemiadis, P. A Review of Robot-Assisted Lower-Limb Stroke Therapy: Unexplored Paths and Future Directions in Gait Rehabilitation. Front. Neurorobot. 2020, 14, 19. [Google Scholar] [CrossRef]
- Sijobert, B.; Azevedo, C.; Pontier, J.; Graf, S.; Fattal, C. A Sensor-Based Multichannel FES System to Control Knee Joint and Reduce Stance Phase Asymmetry in Post-Stroke Gait. Sensors 2021, 21, 2134. [Google Scholar] [CrossRef]
- Li, Y.; Yang, Q.; Fang, P.; Song, R. Adaptive Stimulation Profiles Adjustment of Functional Electrical Stimulation for Foot Drop Based on Iterative Learning Control. In Proceedings of the 2023 International Conference on Advanced Robotics and Mechatronics (ICARM), Sanya, China, 8–10 July 2023; IEEE: Piscataway, NJ, USA, 2023; pp. 895–899. [Google Scholar] [CrossRef]
- Li, Y.; Yang, X.; Zhou, Y.; Chen, J.; Du, M.; Yang, Y. Adaptive Stimulation Profiles Modulation for Foot Drop Correction Using Functional Electrical Stimulation: A Proof of Concept Study. IEEE J. Biomed. Health Inform. 2021, 25, 59–68. [Google Scholar] [CrossRef]
- Dong, H.; Hou, J.; Song, Z.; Xu, R.; Meng, L.; Ming, D. An adaptive reflexive control strategy for walking assistance system based on functional electrical stimulation. Front. Neurosci. 2022, 16, 944291. [Google Scholar] [CrossRef]
- Anaya-Reyes, F.; Narayan, A.; Aguirre-Ollinger, G.; Cheng, H.-J.; Yu, H. An Omnidirectional Assistive Platform Integrated With Functional Electrical Stimulation for Gait Rehabilitation: A Case Study. IEEE Trans. Neural Syst. Rehabil. Eng. 2020, 28, 710–719. [Google Scholar] [CrossRef]
- Zhang, Q.; Meng, Y.; Wu, L.; Xiang, X.; Xiong, C. Artificially induced joint movement control with musculoskeletal model-integrated iterative learning algorithm. Biomed. Signal Process. Control 2020, 59, 101843. [Google Scholar] [CrossRef]
- Hosiasson, M.; Rigotti-Thompson, M.; Appelgren-Gonzalez, J.; Covarrubias-Escudero, F.; Urzua, B.; Barría, P.; Aguilar, R. Biomechanical Gait Effects of a Single Intervention with Wearable Closed Loop Control FES System in Chronic Stroke Patients. A Proof-of-Concept Pilot Study. In Proceedings of the 2023 International Conference on Rehabilitation Robotics (ICORR), Singapore, 24–28 September 2023; IEEE: Piscataway, NJ, USA, 2023; pp. 1–6. [Google Scholar] [CrossRef]
- Yeom, H.; Chang, Y.-H. Autogenic EMG-controlled functional electrical stimulation for ankle dorsiflexion control. J. Neurosci. Methods 2010, 193, 118–125. [Google Scholar] [CrossRef] [PubMed]
- Shaikh, M.F.; Salcic, Z.; Wang, K.I.-K.; Hu, A.P. Bipedal gait model for precise gait recognition and optimal triggering in foot drop stimulator: A proof of concept. Med. Biol. Eng. Comput. 2018, 56, 1731–1746. [Google Scholar] [CrossRef] [PubMed]
- Obuz, S.; Duenas, V.H.; Downey, R.J.; Klotz, J.R.; Dixon, W.E. Closed-Loop Neuromuscular Electrical Stimulation Method Provides Robustness to Unknown Time-Varying Input Delay in Muscle Dynamics. IEEE Trans. Control Syst. Technol. 2020, 28, 2482–2489. [Google Scholar] [CrossRef]
- Xu, S.; Li, C.; Wei, C.; Kang, X.; Shu, S.; Liu, G.; Xu, Z.; Han, M.; Luo, J.; Tang, W. Closed-Loop Wearable Device Network of Intrinsically-Controlled, Bilateral Coordinated Functional Electrical Stimulation for Stroke. Adv. Sci. 2024, 11, 2304763. [Google Scholar] [CrossRef]
- Singh, M.; Sharma, N. Data-driven Model Predictive Control for Drop Foot Correction. In Proceedings of the 2023 American Control Conference (ACC), San Diego, CA, USA, 31 May–2 June 2023; IEEE: Piscataway, NJ, USA, 2023; pp. 2615–2620. [Google Scholar] [CrossRef]
- Watanabe, T.; Tadano, T. Design of Closed-Loop Fuzzy FES Controller and Tests in Controlling Knee Extension Movements. IEICE Trans. Inf. Syst. 2017, E100.D, 2261–2264. [Google Scholar] [CrossRef]
- Hayami, N.; Williams, H.E.; Shibagaki, K.; Vette, A.H.; Suzuki, Y.; Nakazawa, K.; Nomura, T.; Milosevic, M. Development and Validation of a Closed-Loop Functional Electrical Stimulation-Based Controller for Gait Rehabilitation Using a Finite State Machine Model. IEEE Trans. Neural Syst. Rehabil. Eng. 2022, 30, 1642–1651. [Google Scholar] [CrossRef]
- Qian, J.; Rong, K.; Qian, Z.; Wen, C.; Zhang, S. Effects of a multichannel dynamic functional electrical stimulation system on hemiplegic gait and muscle forces. J. Phys. Ther. Sci. 2015, 27, 3541–3544. [Google Scholar] [CrossRef]
- Zhou, H.Y.; Huang, L.K.; Gao, Y.M.; Vasic, Z.L.; Cifrek, M.; Du, M. Estimating the Ankle Angle Induced by FES via the Neural Network-Based Hammerstein Model. IEEE Access 2019, 7, 141277–141286. [Google Scholar] [CrossRef]
- Zhang, Q.; Hayashibe, M.; Azevedo-Coste, C. Evoked Electromyography-Based Closed-Loop Torque Control in Functional Electrical Stimulation. IEEE Trans. Biomed. Eng. 2013, 60, 2299–2307. [Google Scholar] [CrossRef] [PubMed]
- Embrey, D.G.; Holtz, S.L.; Alon, G.; Brandsma, B.A.; McCoy, S.W. Functional Electrical Stimulation to Dorsiflexors and Plantar Flexors During Gait to Improve Walking in Adults With Chronic Hemiplegia. Arch. Phys. Med. Rehabil. 2010, 91, 687–696. [Google Scholar] [CrossRef] [PubMed]
- Chen, W.; Li, J.; Zhu, S.; Zhang, X.; Men, Y.; Wu, H. Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion. Appl. Bionics Biomech. 2022, 2022, 9933018. [Google Scholar] [CrossRef]
- Wiesener, C.; Seel, T.; Spieker, L.; Niedeggen, A.; Schauer, T. Inertial-Sensor-Controlled Functional Electrical Stimulation for Swimming in Paraplegics: Enabling a Novel Hybrid Exercise Modality. IEEE Control Syst. 2020, 40, 117–135. [Google Scholar] [CrossRef]
- Merad, M.; Downey, R.J.; Obuz, S.; Dixon, W.E. Isometric Torque Control for Neuromuscular Electrical Stimulation with Time-Varying Input Delay. IEEE Trans. Control Syst. Technol. 2016, 24, 971–978. [Google Scholar] [CrossRef]
- Seel, T.; Werner, C.; Raisch, J.; Schauer, T. Iterative learning control of a drop foot neuroprosthesis—Generating physiological foot motion in paretic gait by automatic feedback control. Control Eng. Pract. 2016, 48, 87–97. [Google Scholar] [CrossRef]
- Guan, W.; Zhou, L.; Cao, Y. Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm. Complexity 2021, 2021, 6651495. [Google Scholar] [CrossRef]
- Mendes, L.A.; Lima, I.N.; Souza, T.; Nascimento, G.C.D.; Resqueti, V.R.; Fregonezi, G.A. Motor neuroprosthesis for promoting recovery of function after stroke. Cochrane Database Syst. Rev. 2020, 2020, CD012991. [Google Scholar] [CrossRef]
- Duenas, V.H.; Cousin, C.A.; Parikh, A.; Freeborn, P.; Fox, E.J.; Dixon, W.E. Motorized and Functional Electrical Stimulation Induced Cycling via Switched Repetitive Learning Control. IEEE Trans. Control Syst. Technol. 2019, 27, 1468–1479. [Google Scholar] [CrossRef]
- Beaulieu, L.-D.; Massé-Alarie, H.; Brouwer, B.; Schneider, C. Noninvasive neurostimulation in chronic stroke: A double-blind randomized sham-controlled testing of clinical and corticomotor effects. Top. Stroke Rehabil. 2015, 22, 8–17. [Google Scholar] [CrossRef] [PubMed]
- Jochumsen, M.; Niazi, I.K.; Signal, N.; Nedergaard, R.W.; Holt, K.; Haavik, H.; Taylor, D. Pairing Voluntary Movement and Muscle-Located Electrical Stimulation Increases Cortical Excitability. Front. Hum. Neurosci. 2016, 10, 482. [Google Scholar] [CrossRef]
- Li, Z.; Guiraud, D.; Andreu, D.; Gelis, A.; Fattal, C.; Hayashibe, M. Real-Time Closed-Loop Functional Electrical Stimulation Control of Muscle Activation with Evoked Electromyography Feedback for Spinal Cord Injured Patients. Int. J. Neural Syst. 2018, 28, 1750063. [Google Scholar] [CrossRef]
- Li, Z.; Guiraud, D.; Andreu, D.; Benoussaad, M.; Fattal, C.; Hayashibe, M. Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients. J. Neuroeng. Rehabil. 2016, 13, 60. [Google Scholar] [CrossRef] [PubMed]
- Kojović, J.; Miljković, N.; Janković, M.M.; Popović, D.B. Recovery of motor function after stroke: A polymyography-based analysis. J. Neurosci. Methods 2011, 194, 321–328. [Google Scholar] [CrossRef]
- Arcolezi, H.H.; Nunes, W.R.; de Araujo, R.A.; Cerna, S.; Sanches, M.A.; Teixeira, M.C.; de Carvalho, A.A. RISE controller tuning and system identification through machine learning for human lower limb rehabilitation via neuromuscular electrical stimulation. Eng. Appl. Artif. Intell. 2021, 102, 104294. [Google Scholar] [CrossRef]
- Zhang, Q.; Iyer, A.; Sun, Z.; Dodson, A.; Sharma, N. Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation. In Proceedings of the ASME 2020 Dynamic Systems and Control Conference, Virtual, Online, 5–7 October 2020; p. V001T14A003. [Google Scholar] [CrossRef]
- Downey, R.J.; Cheng, T.-H.; Bellman, M.J.; Dixon, W.E. Switched Tracking Control of the Lower Limb During Asynchronous Neuromuscular Electrical Stimulation: Theory and Experiments. IEEE Trans. Cybern. 2017, 47, 1251–1262. [Google Scholar] [CrossRef]
- Zhang, Q.; Iyer, A.; Lambeth, K.; Kim, K.; Sharma, N. Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation. Sensors 2022, 22, 335. [Google Scholar] [CrossRef]
- Jung, J.; Lee, D.-W.; Son, Y.; Kim, B.; Gu, J.; Shin, H.C. Volitional EMG Controlled Wearable FES System for Lower Limb Rehabilitation. In Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico City, Mexico, 1–5 November 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 7099–7102. [Google Scholar] [CrossRef]
- Jung, J.; Lee, D.-W.; Son, Y.K.; Kim, B.S.; Shin, H.C. Volitional EMG Estimation Method during Functional Electrical Stimulation by Dual-Channel Surface EMGs. Sensors 2021, 21, 8015. [Google Scholar] [CrossRef]
- Andreu, D.; Sijobert, B.; Toussaint, M.; Fattal, C.; Azevedo-Coste, C.; Guiraud, D. Wireless Electrical Stimulators and Sensors Network for Closed Loop Control in Rehabilitation. Front. Neurosci. 2020, 14, 117. [Google Scholar] [CrossRef] [PubMed]
- Mrachacz-Kersting, N.; Jiang, N.; Stevenson, A.J.T.; Niazi, I.K.; Kostic, V.; Pavlovic, A.; Radovanovic, S.; Djuric-Jovicic, M.; Agosta, F.; Dremstrup, K.; et al. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J. Neurophysiol. 2016, 115, 1410–1421. [Google Scholar] [CrossRef] [PubMed]
- Hayashibe, M. Evoked Electromyographically Controlled Electrical Stimulation. Front. Neurosci. 2016, 10, 335. [Google Scholar] [CrossRef] [PubMed]
- Benoussaad, M.; Rotella, F.; Chaibi, I. Flatness of musculoskeletal systems under functional electrical stimulation. Med. Biol. Eng. Comput. 2020, 58, 1113–1126. [Google Scholar] [CrossRef]
- Rezaee, Z.; Kobravi, H.R. Human Gait Control Using Functional Electrical Stimulation Based on Controlling the Shank Dynamics. Basic Clin. Neurosci. J. 2020, 11, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Sun, Z.; Iyer, A.; Lambeth, K.; Cleveland, C.; Sharma, N. Knee extension tracking and fatigue regulation results using a robust MPC approach in a hybrid exoskeleton. Control Eng. Pract. 2023, 141, 105717. [Google Scholar] [CrossRef]
- Li, W.; Rovetta, A.; Ding, X.; Chen, L.; Han, Y. Virtual Multi-Interaction for Rehabilitation Robotics. In Proceedings of the 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM), Shenzhen, China, 18–21 December 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 204–208. [Google Scholar] [CrossRef]
- Lim, D.; Pei, W.; Lee, J.W.; Musselman, K.E.; Masani, K. Feasibility of using a depth camera or pressure mat for visual feedback balance training with functional electrical stimulation. Biomed. Eng. Online 2024, 23, 19. [Google Scholar] [CrossRef]
- Pappas, I.P.; Popovic, M.R.; Keller, T.; Dietz, V.; Morari, M. A reliable gait phase detection system. IEEE Trans. Neural Syst. Rehabil. Eng. Publ. IEEE Eng. Med. Biol. Soc. 2001, 9, 113–125. [Google Scholar] [CrossRef] [PubMed]
- Erfanian, A.; Chizeck, H.J.; Hashemi, R.M. Using evoked EMG as a synthetic force sensor of isometric electrically stimulated muscle. IEEE Trans. Biomed. Eng. 1998, 45, 188–202. [Google Scholar] [CrossRef]
- Kutlu, M.; Freeman, C.T.; Hallewell, E.; Hughes, A.-M.; Laila, D.S. Upper-limb stroke rehabilitation using electrode-array based functional electrical stimulation with sensing and control innovations. Med. Eng. Phys. 2016, 38, 366–379. [Google Scholar] [CrossRef]
- Whittle, M.W. Clinical gait analysis: A review. Hum. Mov. Sci. 1996, 15, 369–387. [Google Scholar] [CrossRef]
- Gutierrez-Martinez, J.; Mercado-Gutierrez, J.A.; Carvajal-Gámez, B.E.; Rosas-Trigueros, J.L.; Contreras-Martinez, A.E. Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions. Front. Hum. Neurosci. 2021, 15, 772837. [Google Scholar] [CrossRef]
Author | Subjects | Type of Movement | Purpose | Sensing Technology/Strategy | Measured Variable/Signal | Control Strategy | Parameters Modulated |
---|---|---|---|---|---|---|---|
[20] | Healthy | Sitting | Rehabilitation | EMG and EEG sensors Torque transducers | Real-time control interface of the MAFO | - | |
[21] | Healthy | Walking | Assistance | FSRs IMUs | Ground reaction force Angular rate Acceleration | Reflexive control algorithm | Timing and coordination of muscle activations Stimulation sequences for specific muscles’ stimulation amplitude |
[22] | Healthy | Walking | Rehabilitation | IMUs FSRs | Dynamics of the leg and foot Gait events Ground reaction forces | CL | Pulse width |
[23] | Both | Sitting | Rehabilitation and Assistance | Load cell | Torque generated by the muscle contractions Knee joint angle | CL, PID | - |
[24] | - | Sit-to-stand | Assistance | - | - | Combination of sliding mode control and wavelet networks | - |
[25] | Patients | FES cycling | Rehabilitation | IMUs | Angular velocity Linear acceleration | Adaptive method | Timing of muscle contractions |
[26] | Healthy and patient | Walking and sitting | Rehabilitation | sEMG sensors Torque sensors Kinematic sensors | Joint angles Angular velocity Acceleration | Linear control schemes | Stimulation intensity Pulse width Frequency |
[27] | Patients | Walking | Rehabilitation | IMUs | Angular displacements | CL, FSM | Stimulation amplitude |
[28] | Healthy | Sitting | Rehabilitation and Assistance | Force transducers | Force | CL | Stimulation amplitude |
[29] | Patients | Walking/sitting | Rehabilitation | EMG/EEG | - | - | |
[30] | Patients | Walking | Rehabilitation | IMUs Force sensors | Knee angle Gait events | CL, Proportional controller | Pulse width |
[31] | Healthy | Walking and sitting | Rehabilitation | Motion capture system | Ankle joint angle | CL, ILC | Pulse width Stimulation intensity |
[32] | Both | Walking | Rehabilitation | sEMG sensors Pressure Sensors | Angular velocity Ground reaction forces | CL, NS | Stimulation intensity |
[33] | Healthy | Walking | Assistance | FSRs IMUs | Sagittal shank angle Angular velocity Acceleration | CL, FSM | Time coefficient parameter (τ) and pulse width |
[34] | Patients | Walking | Rehabilitation | IMUs | Angular velocity Acceleration | CL, Proportional controller | Pulse width |
[35] | Healthy | Sitting | Rehabilitation | Goniometer | Knee joint | Musculoskeletal model-integrated iterative learning control (MMILC) | Pulse width |
[36] | Patients | Walking | Rehabilitation | IMUs | Foot pose initiation of the subject’s step | CL, NS | Yes, NS |
[37] | Healthy | Sitting | Rehabilitation | sEMG sensors | Volitional electrical activity of the muscles | CL, Gram–Schmidt filtering algorithm | Waveforms proportional to the measured vEMG envelope |
[38] | Healthy | Walking | Rehabilitation | IMUs FSRs | Heel pressure, shank tilt, and foot rotations | CL, FSM | Yes, NS |
[39] | Healthy | Sitting | Rehabilitation | Optical encoders | Knee joint angle | CL | Pulse amplitude |
[40] | Patients | Walking and sitting | Rehabilitation and Assistance | sEMG sensors IMUs | Angles of the ankles and feet Electrical activity of muscles | CL, NS | PWM signals |
[41] | Patient | Walking and sitting | Rehabilitation | IMUs | Foot pitch Roll rate state | CL, NS | - |
[42] | Healthy | Sitting | Rehabilitation | IMUs | knee joint angle | CL fuzzy controller | Yes, NS |
[43] | Healthy | Walking | Rehabilitation | sEMG sensors Ground reaction forces Motion capture | Electrical activity Timing of heel-strikes hip and knee joint | CL, FSM | Timing and sequence |
[44] | Patients | Walking | Rehabilitation | Motion Analysis system | Muscle Forces Step length Ankle angles | OL | No |
[45] | Healthy | Sitting | Rehabilitation | IMUs | ankle angle | neural network-based H model | Pulse amplitude |
[17] | Healthy | Walking | Rehabilitation | IMUs Motion Capture System | Angular velocity | FSM | No |
[46] | Healthy | Sitting | Rehabilitation | sEMG sensors | joint torque | CL, NS | Yes, NS |
[7] | Patients | Walking | Assistance | IMUs | Foot angle and angular velocity | CL, NS | Pulse width |
[47] | Patients | Walking | Rehabilitation | Forefoot switch accelerometer | Timing and intensity of muscle contractions | No | |
[48] | Both | Rehabilitation | Video-based motion capture/ IMUs/ Force-sensing resistance sensors/ EEG-sEMG sensors | Motion law of joints/ joint acceleration, velocity and angular acceleration/ planta pressure/ bioelectrical information | - | - | |
[3] | Healthy | Sitting | Rehabilitation | Encoder sEMG sensors | Knee joint angle, angular velocity, and acceleration | OL | - |
[49] | Patients | Swiming | Assistance | IMUs | Trunk roll angle and rate Upper-arm inclination angle Knee joint angle Torso roll angle | CL and OL | - |
[50] | Healthy | Sitting | Rehabilitation and Assistance | Leg extension machine | Torque produced about the knee joint | CL nonlinear control | Pulse width |
[51] | Patients | Walking and sitting | Rehabilitation | IMUs | Accelerations, Angular rates of the foot | CL, ILC | Stimulation intensity and timing |
[52] | Patients | Walking | Rehabilitation | angle sensors | angles of the knee joint | CL, ILC | Pulse sequences |
[53] | Patients | Walking | Rehabilitation | Heel switches, accelerometers, and tilt sensors | Gait symmetry, rhythmicity, and ankle movements | CL, NS | Duration and intensity of the electrical stimulation |
[54] | Both | Cycling | Rehabilitation | sEMG Optical encoder | Electrical activity Crank position | CL repetitive learning control | Pulse width |
[55] | Patients | Sitting | Rehabilitation | Goniometers Dynamometers sEMG sensors | - | - | |
[56] | Healthy | Sitting | Rehabilitation | sEMG sensors | Motor-evoked potentials (MEPs) | OL | No |
[4] | Patients | Walking | Rehabilitation and Assistance | Electrode pads | Angle of ankle dorsiflexion Position Electrical activity of muscles | CL, RC | Pulse width |
[57] | Healthy | Walking | Rehabilitation | sEMG sensors | M-wave | CL model predictive control (MPC) | Pulse width |
[58] | Both | Sitting | Rehabilitation | sEMG sensors Dynamometer | Evoked electromyography Ankle joint torque | - | - |
[59] | Both | Sitting | Assistance | EMG amplifiers IMUs goniometers | Muscle activation patterns, Joint angles | - | - |
[60] | Both | Standing | Rehabilitation | Electrogoniometer Gyroscope Accelerometers | Angular position of lower limb | CL RISE control strategy | - |
[61] | Healthy | Sitting | Rehabilitation | US imaging | Delayed muscle activation | CL, NS | - |
[62] | Healthy | Standing | Rehabilitation | force transducers optical encoders | torque produced about the knee-joint and the knee-joint angle | CL, asynchronous stimulation controller | Pulse width Stimulation channels |
[63] | Healthy | Sitting | Rehabilitation | US | US echogenicity signal | CL, NS | Pulse width threshold and saturation values |
[15] | Healthy | Walking | Rehabilitation | US Force plates IMUs | US echogenicity Ground reaction force 2D motion in the sagittal plane | CL, NS | Pulse width |
[64] | Healthy | Squat/heel lift/walking | Rehabilitation | sEMG sensors IMUs | Motion intention Joint angle | CL, NS | Yes, NS |
[65] | Healthy | Sitting | Rehabilitation and Assistance | sEMG sensors | Muscle activity | - | - |
[66] | Patients | Walking | Rehabilitation | Distributed Measurement Units (DMU) | Knee joint angle Gait phases | CL, NS | Yes, NS |
[44] | Patients | Walking | Rehabilitation | Foot switch Motion capture | Heel strike and toe off Muscle forces Step length Maximum knee and ankle angles | OL | - |
[67] | Patient | Walking and sitting | Rehabilitation | Surface electrodes IMUs | EEG and EMG Tapping frequency of finger or foot | CL | - |
[68] | - | - | Rehabilitation | EMG | Evoked EMG | CL | - |
[69] | Simulation | - | Rehabilitation | - | - | OL | - |
[70] | Healthy | Walking | Rehabilitation | Motion analyzer | Shank and thigh movement | Fuzzy controller | Stimulation intensity |
[71] | Both | Walking and sitting | Assisted | Dynamometer | Torque | RISE controller | - |
[72] | Patient | Walking | Rehabilitation | IMUs Force sensor | Posture information of limb movement Specific movement of limb | OL | - |
[73] | Healthy | Standing balance exercise | Rehabilitation | Depth camera Pressure mat | Center of mass Center of pressure | - | - |
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González-Graniel, E.; Mercado-Gutierrez, J.A.; Martínez-Díaz, S.; Castro-Liera, I.; Santillan-Mendez, I.M.; Yanez-Suarez, O.; Quiñones-Uriostegui, I.; Rodríguez-Reyes, G. Sensing and Control Strategies Used in FES Systems Aimed at Assistance and Rehabilitation of Foot Drop: A Systematic Literature Review. J. Pers. Med. 2024, 14, 874. https://doi.org/10.3390/jpm14080874
González-Graniel E, Mercado-Gutierrez JA, Martínez-Díaz S, Castro-Liera I, Santillan-Mendez IM, Yanez-Suarez O, Quiñones-Uriostegui I, Rodríguez-Reyes G. Sensing and Control Strategies Used in FES Systems Aimed at Assistance and Rehabilitation of Foot Drop: A Systematic Literature Review. Journal of Personalized Medicine. 2024; 14(8):874. https://doi.org/10.3390/jpm14080874
Chicago/Turabian StyleGonzález-Graniel, Estefanía, Jorge A. Mercado-Gutierrez, Saúl Martínez-Díaz, Iliana Castro-Liera, Israel M. Santillan-Mendez, Oscar Yanez-Suarez, Ivett Quiñones-Uriostegui, and Gerardo Rodríguez-Reyes. 2024. "Sensing and Control Strategies Used in FES Systems Aimed at Assistance and Rehabilitation of Foot Drop: A Systematic Literature Review" Journal of Personalized Medicine 14, no. 8: 874. https://doi.org/10.3390/jpm14080874