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Sensorimotor and Cognitive Wearable Augmentation Devices

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (25 April 2023) | Viewed by 8782

Special Issue Editors


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Guest Editor
Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4710-057 Braga, Portugal
Interests: human motion; human locomotion; human–robot interactions and collaboration; medical devices; neuro-rehabilitation of patients suffering from motor problems by means of bio-inspired robotics and neuroscience technologies
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4710-057 Braga, Portuga
Interests: neurorehabilitation robotics; human–robot interaction; motion control; wearable sensors; smart textiles; motion analysis and recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable technology is an emerging trend of personal devices that integrates electronics, which can be worn on any part of the body, to fit challenges in our lifestyles. Wearable robots can be used to augment human motor functions and enhance physical capabilities, typically in manipulation and locomotion tasks. Motor augmentative technologies may include (i) exoskeletons that mirror the human kinematic structure to enhance his/her capabilities beyond the natural ones; (ii) prosthesis that physically substitutes missing limb; and (iii) supernumerary robotic limbs that enable additional degrees of freedom to empower human movements. There is also increased interest in user-oriented wearable devices to make up for a loss-of-sensory function, including somatosensory and sense of touch, as a result of a spectrum of injuries and diseases. Augmented reality and haptic wearables have stood out to address sensory impairments. Together with the design of sensorimotor augmentation devices, technologies that target the cognitive augmentation of human capabilities are rising. Cognitive augmentation focuses on the improvement of the processes of acquiring/generating knowledge and understanding the world around us. Overall, these technologies can augment and enhance humans in a variety of contexts, such as in medical, industrial, and consumer domains. This Special Issue aims to present the latest results and future roles in emerging wearable technologies to augment and enhance humans in sensorimotor and cognitive functions. Contributions may include but are not limited to presenting control–feedback interfaces; the bilateral interface between the robot and the human; the effects and benchmarks of augmentative technologies. It also covers understanding the complexity of designing wearable, intuitive, and non-intrusive devices for real applications.

Dr. Cristina P. Santos
Dr. Joana Figueiredo
Guest Editors

Manuscript Submission Information

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Keywords

  • human augmentation
  • sensorimotor augmentation
  • cognitive augmentation
  • exoskeletons
  • prosthesis
  • supernumerary robotic limbs
  • haptic and visual feedback
  • augmented reality
  • human and environment sensing
  • wearable sensors and robots

Published Papers (3 papers)

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Research

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20 pages, 1251 KiB  
Article
IMU-Based Classification of Locomotion Modes, Transitions, and Gait Phases with Convolutional Recurrent Neural Networks
by Daniel Marcos Mazon, Marc Groefsema, Lambert R. B. Schomaker and Raffaella Carloni
Sensors 2022, 22(22), 8871; https://doi.org/10.3390/s22228871 - 16 Nov 2022
Cited by 4 | Viewed by 2580
Abstract
This paper focuses on the classification of seven locomotion modes (sitting, standing, level ground walking, ramp ascent and descent, stair ascent and descent), the transitions among these modes, and the gait phases within each mode, by only using data in the frequency domain [...] Read more.
This paper focuses on the classification of seven locomotion modes (sitting, standing, level ground walking, ramp ascent and descent, stair ascent and descent), the transitions among these modes, and the gait phases within each mode, by only using data in the frequency domain from one or two inertial measurement units. Different deep neural network configurations are investigated and compared by combining convolutional and recurrent layers. The results show that a system composed of a convolutional neural network followed by a long short-term memory network is able to classify with a mean F1-score of 0.89 and 0.91 for ten healthy subjects, and of 0.92 and 0.95 for one osseointegrated transfemoral amputee subject (excluding the gait phases because they are not labeled in the data-set), using one and two inertial measurement units, respectively, with a 5-fold cross-validation. The promising results obtained in this study pave the way for using deep learning for the control of transfemoral prostheses with a minimum number of inertial measurement units. Full article
(This article belongs to the Special Issue Sensorimotor and Cognitive Wearable Augmentation Devices)
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15 pages, 3239 KiB  
Article
Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors
by João M. Lopes, Joana Figueiredo, Pedro Fonseca, João J. Cerqueira, João P. Vilas-Boas and Cristina P. Santos
Sensors 2022, 22(20), 7913; https://doi.org/10.3390/s22207913 - 18 Oct 2022
Cited by 9 | Viewed by 2204
Abstract
Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness [...] Read more.
Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ > 0.85) between target and estimation (R¯2 = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control. Full article
(This article belongs to the Special Issue Sensorimotor and Cognitive Wearable Augmentation Devices)
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Review

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22 pages, 432 KiB  
Review
Robotic Biofeedback for Post-Stroke Gait Rehabilitation: A Scoping Review
by Cristiana Pinheiro, Joana Figueiredo, João Cerqueira and Cristina P. Santos
Sensors 2022, 22(19), 7197; https://doi.org/10.3390/s22197197 - 22 Sep 2022
Cited by 6 | Viewed by 2728
Abstract
This review aims to recommend directions for future research on robotic biofeedback towards prompt post-stroke gait rehabilitation by investigating the technical and clinical specifications of biofeedback systems (BSs), including the complementary use with assistive devices and/or physiotherapist-oriented cues. A literature search was conducted [...] Read more.
This review aims to recommend directions for future research on robotic biofeedback towards prompt post-stroke gait rehabilitation by investigating the technical and clinical specifications of biofeedback systems (BSs), including the complementary use with assistive devices and/or physiotherapist-oriented cues. A literature search was conducted from January 2019 to September 2022 on Cochrane, Embase, PubMed, PEDro, Scopus, and Web of Science databases. Data regarding technical (sensors, biofeedback parameters, actuators, control strategies, assistive devices, physiotherapist-oriented cues) and clinical (participants’ characteristics, protocols, outcome measures, BSs’ effects) specifications of BSs were extracted from the relevant studies. A total of 31 studies were reviewed, which included 660 stroke survivors. Most studies reported visual biofeedback driven according to the comparison between real-time kinetic or spatiotemporal data from wearable sensors and a threshold. Most studies achieved statistically significant improvements on sensor-based and clinical outcomes between at least two evaluation time points. Future research should study the effectiveness of using multiple wearable sensors and actuators to provide personalized biofeedback to users with multiple sensorimotor deficits. There is space to explore BSs complementing different assistive devices and physiotherapist-oriented cues according to their needs. There is a lack of randomized-controlled studies to explore post-stroke stage, mental and sensory effects of BSs. Full article
(This article belongs to the Special Issue Sensorimotor and Cognitive Wearable Augmentation Devices)
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