An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton

He, Yongtian; Nathan, Kevin; Venkatakrishnan, Anusha; Rovekamp, Roger N.; Beck, Christopher; Ozdemir, Recep A.; Francisco, Gerard E.; Contreras-Vidal, José L.

doi:10.1109/embc.2014.6944497

Cited by 33 publications

(21 citation statements)

References 17 publications

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“…“Manufacturers” are companies (ReWalk, Indego, Ekso, etc. ), institutions (NASA X1,14 Kinesis,63 etc. ), and many other labs and startups whose prototypes are still in early development.…”

Section: Regulations and Standardsmentioning

confidence: 99%

Risk management and regulations for lower limb medical exoskeletons: a review

He¹,

Eguren²,

Luu³

et al. 2017

MDER

119

121

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Gait disability is a major health care problem worldwide. Powered exoskeletons have recently emerged as devices that can enable users with gait disabilities to ambulate in an upright posture, and potentially bring other clinical benefits. In 2014, the US Food and Drug Administration approved marketing of the ReWalk™ Personal Exoskeleton as a class II medical device with special controls. Since then, Indego™ and Ekso™ have also received regulatory approval. With similar trends worldwide, this industry is likely to grow rapidly. On the other hand, the regulatory science of powered exoskeletons is still developing. The type and extent of probable risks of these devices are yet to be understood, and industry standards are yet to be developed. To address this gap, Manufacturer and User Facility Device Experience, Clinicaltrials.gov, and PubMed databases were searched for reports of adverse events and inclusion and exclusion criteria involving the use of lower limb powered exoskeletons. Current inclusion and exclusion criteria, which can determine probable risks, were found to be diverse. Reported adverse events and identified risks of current devices are also wide-ranging. In light of these findings, current regulations, standards, and regulatory procedures for medical device applications in the USA, Europe, and Japan were also compared. There is a need to raise awareness of probable risks associated with the use of powered exoskeletons and to develop adequate countermeasures, standards, and regulations for these human-machine systems. With appropriate risk mitigation strategies, adequate standards, comprehensive reporting of adverse events, and regulatory oversight, powered exoskeletons may one day allow individuals with gait disabilities to safely and independently ambulate.

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Section: Regulations and Standardsmentioning

confidence: 99%

Risk management and regulations for lower limb medical exoskeletons: a review

He¹,

Eguren²,

Luu³

et al. 2017

MDER

119

121

View full text Add to dashboard Cite

show abstract

“…Stroke etiology was reported in 92 cases: 72 of which were ischemic in origin, 18 were hemorrhagic, and two were ischemic/hemorrhagic. Six studies did not provide information related to stroke type (Ping et al, 2013 ; He et al, 2014 ; Androwis et al, 2018 ; Sloot et al, 2018 ). The time from stroke to study participation was reported for 98 patients, with the majority ( N = 57) recruited in the chronic phase of stroke.…”

Section: Resultsmentioning

confidence: 99%

“…Three studies identified the participants as “walkers” but did not specify the level of assistance required, if any (Chisari et al, 2015 ; Srivastava et al, 2016 ; Sloot et al, 2018 ). Other studies described the participants' mean motor subscale score of the Functional Independence Measure (Androwis et al, 2018 ) [26 ± 4; where 13–38 indicate low scores for motor independence as guided by Itaya et al ( 2017 )], the Fugl–Meyer Lower Limb Scale (He et al, 2014 ) [12/34; where a cutoff score of <21 indicates lower mobility levels, as guided by Kwong and Ng ( 2019 )], or strength of the lower limb tibialis anterior muscles of <4 on the MRC scale (Gandolla et al, 2018 ) or specified the level of assistance required to walk (Hesse et al, 2010 ; Ping et al, 2013 ). Two studies did not address the participants' walking status (Fan and Yin, 2013 ; Contreras-Vidal et al, 2018 ); however, one of these studies used the 6 MWT as a baseline score.…”

Section: Resultsmentioning

confidence: 99%

“…Three of the thirteen studies included in this review recorded and analyzed EEG activity. As summarized in Table 1 , two studies used EEG during robotic gait to decode gait kinematics (He et al, 2014 ; Contreras-Vidal et al, 2018 ) and muscle activity during walking (He et al, 2014 ). One study used EEG to determine frontoparietal connectivity as an outcome measure of neuroplasticity following a robotic gait training intervention (Calabrò et al, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

“…The classification accuracy for predicting joint angles during gait was noted to improve with multiple training sessions and gait speed (Contreras-Vidal et al, 2018). The pilot study conducted by He et al, using a 10th-order unscented Kalman filter, demonstrated similar moderately high online decoding accuracies for joint kinematics during robotic gait but not for muscle activity patterns during robotic gait training in two healthy participants and one stroke survivor (He et al, 2014) using a multimodal interface comprising EEG [64channel EEG (actiCap system, Brain Products GmbH, Munich, Germany)], EMG, and motion (goniometers), instrumented in conjunction with the X1 exoskeleton employed during 5-min overground walking sessions of three conditions: no robot, robot off (X1 in passive mode), and robot on (X1 in active mode). The final EEG-based study, an RCT by Calabrò et al (N = 40 sub-acute and chronic stroke patients), employed 21-channel EEG as a measure of neuroplasticity using frontoparietal effective connectivity (FPEC) but did not interface with the robotic device directly.…”

Section: Eeg-based Studiesmentioning

confidence: 98%

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A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

et al. 2020

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Background: Stroke is a disease with a high associated disability burden. Robotic-assisted gait training offers an opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG), or electromyography (EMG) can offer new strategies for robotic gait re-education after a stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and the limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8,899 papers identified, 13 articles were considered for the final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait, two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG-related gait data, no EEG study has been identified to close the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle, and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualized robot-driven step trajectory. Lennon et al. Stroke: Neural Interfaced Robotic Walking Conclusions: Wide heterogeneity in the reporting and the purpose of neurobiosignal use during robotic gait training after a stroke exists. Neural interfacing with robotic gait after a stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after a stroke would benefit from a more standardized protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to the study type and the participants' characteristics.

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Brain-Computer Interfaces for Motor Rehabilitation

Rupp

2018

Handbook of Human Motion

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An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton

Cited by 33 publications

References 17 publications

Risk management and regulations for lower limb medical exoskeletons: a review

Risk management and regulations for lower limb medical exoskeletons: a review

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

Brain-Computer Interfaces for Motor Rehabilitation

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