Frontier Research Overview
“The ability to restore and improve movements for individuals with walking difficulty is extremely crucial to improve the quality of their life.”
From this point of view, we focus on developing an adaptive biologically-inspired control mechanism with fast real-time online adaptation of an exoskeleton system to achieve adaptive, dynamic, and robust user-exoskeleton interaction. This will result in natural and energy-efficient walking of patients as well as multiple gait generation for walking on a level floor, walking up/downstairs, and walking on uneven terrain.
This video demonstrates early results of our developed EXOBIC system for advanced rehabilitation research. The EXOBIC system integrates lower-limb exoskeleton, biofeedback, and automated gait lab technologies. Our adaptive neural control with an automatic training process allows the exoskeleton to quickly learn and generate personalized gaits of individual subjects . Biofeedback (such as brainwave signals and muscle electrical signals) from electromyography (EMG) and electroencephalography (EEG) sensors and monitored gait movement feedback from the automated gait lab are being integrated into the exoskeleton system to improve exoskeleton autonomy as well as human-exoskeleton coordination for the rehabilitation of moderately and severely damaged patients with neurological problems and strokes. Artificial neural networks with deep metric learning and a multi-task autoencoder (MIN2Net ) are developed for combining the multi-modal feedback information, while a wireless network is used for exoskeleton-biosensors-gait lab communications and edge-cloud computing. Wearable exoskeletons with biofeedback for gait rehabilitation are still in the early stages of development, and randomized control trials will be needed to show therapeutic efficacy. The introduced EXOBIC system can contribute to a journey of our robotics community for efficient gait rehabilitation and restoration of motor functions toward making robots for humans.
Akkawutvanich, C.; Ketrungsri, W.; Sricom, N.; Rungsilp, C.; Chaisaen, R.; Kiatthaveephong, S.; Sitthiwanit, T.; Wilaiprasitporn, T.; Manoonpong, P. (2023) EXOBIC: Intelligent Exoskeleton with Biofeedback for Advanced Rehabilitation, Stand-alone video, 2023 IEEE International Conference on Robotics and Automation (ICRA)
This study was supported by IST-Human centric automation technology of VISTEC (IST-DEMO, POM-Robotics 300/111500/221111500203)
 Akkawutvanich, C. and Manoonpong, P., 2023. Personalized Symmetrical and Asymmetrical Gait
Generation of a Lower-limb Exoskeleton. IEEE Transactions on Industrial Informatics, DOI:
 Autthasan, P., et al., 2021. MIN2net: End-to-end multi-task learning for subject-independent motor
imagery EEG classification. IEEE Transactions on Biomedical Engineering, 69(6), pp.2105-2118.
Neural Multimodal Control for Versatile Motion Generation and Continuous Transitions of a Lower-limb Exoskeleton
The paper proposes a neural multimodal control for versatile motion generation and continuous transitions between activities of a mobile lower-limb exoskeleton. The control method combines neural rhythmic oscillators as central pattern generators (CPGs) for basic rhythmic pattern generation and shaping networks using the radial basis function (RBF) networks to encode different demonstrated patterns e.g., sit-to-stand, climbing up the stairs, and walking on a level floor and a treadmill. Additionally, single recurrent neurons are used as low-pass filters to ensure smoothness. This approach is verified on a real exoskeleton in both static and dynamic situations. The experiment results show successful assistance with frequency adaptation to deal with different movement speeds.
Akkawutvanich C, Sricom N, and Manoonpong P. Neural Multimodal Control for Versatile Motion Generation and Continuous Transitions of a Lower-limb Exoskeleton 26th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR 2023) 2023. (Accepted)
Personalized Symmetrical and Asymmetrical Gait Generation of a Lower-limb Exoskeleton:
Personal assistive devices for rehabilitation will be in increasing demand during the coming decades due to demographic change, i.e., an aging society. Among the elderly population, difficulty in walking is the most common problem. Even though there are commercially available lower-limb exoskeleton systems, the coordination between user and device still needs to be improved to achieve versatile personalized gaits. To tackle this issue, an advanced EXOskeleton framework for Versatile personalized gaIt generation with a Seamless user-exo interface (called “EXOVIS”) is proposed in this study. The main control of the framework uses adaptive bio-inspired modular neural mechanisms. These mechanisms include decoupled central pattern generators (CPGs) with Hebbian-based synaptic plasticity and adaptive CPG post-processing networks with error-based learning. The control method facilitates the rapid online learning of personalized walking gaits described by the walking frequency as well as hip, knee, and ankle joint patterns. The method is verified on a real lower-limb exoskeleton system with six degrees of freedom (DOFs) on different subjects under static and dynamic conditions such as flat terrain and a split-belt treadmill. The results show that the proposed method can not only automatically learn to generate personalized symmetrical gaits, but also asymmetrical gaits, which have not been explicitly shown by other approaches so far.
Akkawutvanich C and Manoonpong P. Personalized Symmetrical and Asymmetrical Gait Generation of a Lower Limb Exoskeleton. IEEE Transactions on Industrial Informatics. 2023; 19:9798-808
We would like to thank Technaid S.L. for technical support. This work was supported by the VISTEC research grant under the EXOVIS project (Grant No. I20POM-INT010 [PM]) and PTT-RAII under the VIS-RA project (Grant No. 18POM-PTT010 [PM].
EXOBIC – Intelligent EXOskeleton with BIofeedbaCk for Advanced Rehabilitation:
An exoskeleton is one of the medical wearable robotic devices for humans. Our type is a lower-limb exoskeleton type controlling the movement of all legs’ joints. It usually uses in rehabilitation for subjects with conditions ranging from muscle weakness to movement disability. Many studies have shown that brain-computer interface (BCI) can assist to improve the damaged nervous system by reshaping the neuron connectivity (brain plasticity) which is beneficial in rehabilitation. Due to this fact, the purpose of our project is to create an intelligent exoskeleton based on those two technologies. We will enable the exoskeleton to exploit biofeedback signals i.e., Electroencephalogram (EEG), Electromyography (EMG), biomechanical movement, and forces, in order to optimally control the exoskeleton for each individual person. Moreover, this enhanced exoskeleton will be more useful when working seamlessly under an automated gait lab environment resulting in an advanced rehabilitation platform.
This project comprises three main technologies:
- one study is going to create a neural control-based personalized exoskeleton with seamless human-machine interaction (HMI),
- one study is going to create artificial neural networks that can interpret biofeedback signals and translate the information into proper control signals, and
- one study is going to create an automated gait lab where all instruments are well-integrated, better in communication, and suitable for cloud-based solutions.
Application and expected impact:
We hope that this knowledge and innovation will improve the quality of life of vulnerable groups as mentioned. Specifically, we expect the device to be a solution or an alternative choice for disabled persons with orthoses (approx. 25% of 1.5 M disabled persons in Thailand) and also for the impact of demographic change to be an aging society (more than 50 yrs. from 33.7% in 2019 to 42.5% in 2030 )
In terms of the economy, this study related to an orthotic device which has a proportion within around 6.6% of the exported medical devices (2020; Durable items; approximately 10.5 B THB of 159.9 B THB ). The payback period is estimated to be around 2-3 years when we try to model it to be deployed in a clinical rehabilitation center.
 Udomkerdmongkol, Manop, “Thailand Economic Focus: Demographic change in Thailand: How planners can prepare for the future | United Nations in Thailand,” Oct. 19, 2020. https://thailand.un.org/en/96303-thailand-economic-focus-demographic-change-thailand-how-planners-can-prepare-future (accessed Apr. 06, 2021).
 http://medicaldevices.oie.go.th/ (accessed Nov. 30, 2022)
From Bipedal Locomotion to Exoskeleton:
Achieving adaptive, stable, and robust bipedal locomotion and dealing with asymmetrical conditions in a robotic system remain a challenging problem. To address the problem, the research team of the BRAIN lab at IST in collaboration with the University of Southern Denmark has recently proposed novel bio-inspired adaptive motor control. We demonstrate that this real-time motor control can effectively generate adaptive and stable bipedal locomotion with robustness against sensory feedback malfunction for a biped robot. It also allows the robot to effectively walk on a treadmill at different speeds and deal with asymmetric conditions such as weight imbalance and asymmetrical elastic resistance in the legs. As an application of this control technology, we have now successfully applied it to a lower-limb exoskeleton system for gait rehabilitation. This research was supported by PTT-RAII under the VISRA project (AdVanced Human-MachIne InteractionS Technology for ImpRoving QuAlity of Life and Health).
Akkawutvanich C, Knudsen FI, Riis AF, Larsen JC, and Manoonpong P. Adaptive parallel reflex- and decoupled CPG-based control for complex bipedal locomotion. Robotics and Autonomous Systems. 2020 Dec 1; 134:103663.
Video link of the bipedal experiments: