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Smart Machines: Applications and Advances in Human Motion Analysis

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A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: closed (25 December 2022) | Viewed by 16198

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Special Issue Editor

Dr. Cristina P. Santos

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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
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Special Issue Information

Dear Colleagues,

New directions in human motion cover motion recognition and prediction, and human-robot interaction perception using sensor-based technologies driven by recent technological advances (wearable sensors, advanced sensors, artificial intelligence and machine learning, electronic and smart sensing textiles, and so on). Advanced applications in both scenarios rely on the combination of smart sensors with the algorithmic advances in artificial intelligence.

The adequate provision of human motion requires the consideration of various aspects, as follows. The use of unobtrusive, low-cost wearable sensors that are capable of tracking relevant motion in free-living conditions; machine learning-based strategies for reasoning sensor data; intuitive and collaborative sensor-based technologies for timely assisting and interacting with users in various environments. The accurate decision making of human motion may bring new achievements in diverse robotic applications. The inclusion of motion prediction strategies in robotic assistive devices is necessary to provide patients with personalized motor assistance and to prevent risk situations. Moreover, the collaborative robots, used in Industry 4.0 programs and social robots, will benefit if the robot is being continuously kept informed of the human motor performance and safety.

This Special Issue covers new strategies to recognize and predict the human motion or the human-robot interaction, both in the clinical and in the industry fields, thanks to the application of smart sensors or the innovative use of the standard wearable sensors. Biofeedback strategies-related sensors to augment human collaboration with robotic systems are also encouraged.

Contributions may include, but are not limited to:

Smart sensors for human motion analysis;
Sensors for decision making and smart-based applications;
Wearable sensor-based strategies for motion intention recognition;
Machine learning algorithms for human motion recognition and prediction;
Machine learning -based sensor measurements for human motion estimation;
Sensors applications on collaborative and assistive robots;
Advanced strategies for improving human-robot interaction;
Sensing for physical human-robot interaction;
Applications of sensors for robotics

You may choose our Joint Special Issue in Sensors.

Dr. Cristina P. Santos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

27 pages, 3477 KiB

Open AccessEditor’s ChoiceArticle

An Integrated Application of Motion Sensing and Eye Movement Tracking Techniques in Perceiving User Behaviors in a Large Display Interaction

by Xiaolong Lou, Lili Fu, Xuanbai Song, Mengzhen Ma, Preben Hansen, Yaqin Zhao and Yujie Duan

Machines 2023, 11(1), 73; https://doi.org/10.3390/machines11010073 - 06 Jan 2023

Cited by 1 | Viewed by 1293

Abstract

In public use of a large display, it is a usual phenomenon that multiple users individually participate in respective tasks on a common device. Previous studies have categorized such activity as independent interaction that involves little group engagement. However, by investigating how users approach, participate in, and interact with large displays, we found that parallel use is affected by group factors such as group size and between-user relationship. To gain a thorough understanding of individual and group behaviors, as well as parallel interaction task performance, one 70-inch display-based information searching task and experiment was conducted, in which a mobile eye movement tracking headset and a motion sensing RGB-depth sensor were simultaneously applied. The results showed that (1) a larger group size had a negative influence on the group users’ concentration on the task, perceived usability, and user experience; (2) a close relationship between users contributed to occasional collaborations, which was found to improve the users’ task completion time efficiency and their satisfaction on the large display user experience. This study proves that an integrated application of eye movement tracking and motion sensing is capable of understanding individual and group users’ behaviors simultaneously, and thus is a valid and reliable scheme in monitoring public activities that can be widely used in public large display systems. Full article

(This article belongs to the Special Issue Smart Machines: Applications and Advances in Human Motion Analysis)

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12 pages, 2739 KiB

Open AccessArticle

Muscle Selection Using ICA Clustering and Phase Variable Method for Transfemoral Amputees Estimation of Lower Limb Joint Angles

by Xingyu Liu, Qing Wei, Hongxu Ma, Honglei An and Yi Liu

Machines 2022, 10(10), 944; https://doi.org/10.3390/machines10100944 - 17 Oct 2022

Cited by 1 | Viewed by 1499

Abstract

Surface electromyography(sEMG) signals are used extensively in the study of lower limb locomotion, capturing and extracting information from various lower limb muscles as input for powered prostheses. Many transfemoral amputees have their lower limbs completely removed below the knee due to disease, accident or trauma. The patients only have the muscles of the thigh and cannot use the muscles of the lower leg as a signal source for sEMG. In addition, wearing sEMG sensors can cause discomfort to the wearer. Therefore, the number of sensors needs to be minimized while ensuring recognition accuracy. In this paper, we propose a novel framework to select the position of sensors and predict joint angles according to the sEMG signals from thigh muscles. Specifically, a method using ICA clustering is proposed to statistically analyze the similarity between muscles. Additionally, a mapping relationship between sEMG and lower limb joint angles is established by combining the BP network and phase variable method, compared with the mapping using only neural networks. The results show that the proposed method has higher estimation accuracy in most of the combinations. The best muscle combination is vastus lateralis (VL) + biceps femoris (BF) + gracilis (GC) (

γ_{k n e e}

= 0.989,

γ_{a n k l e}

= 0.985). The proposed method will be applied to lower limb-powered prostheses for continuous bioelectric control. Full article

(This article belongs to the Special Issue Smart Machines: Applications and Advances in Human Motion Analysis)

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15 pages, 8002 KiB

Open AccessEditor’s ChoiceArticle

A Hybrid Mechanism-Based Robot for End-Traction Lower Limb Rehabilitation: Design, Analysis and Experimental Evaluation

by Lipeng Wang, Junjie Tian, Jiazheng Du, Siyuan Zheng, Jianye Niu, Zhengyan Zhang and Jiang Wu

Machines 2022, 10(2), 99; https://doi.org/10.3390/machines10020099 - 27 Jan 2022

Cited by 11 | Viewed by 2667

Abstract

Conventional lower-limb rehabilitation robots cannot provide in-time rehabilitation training for stroke patients in the acute stage due to their large size and mass as well as their complex wearing process. Aiming to solve the problems, first, a novel hybrid end-traction lower-limb rehabilitation robot (HE-LRR) was designed as the lower-limb rehabilitation requirement of patients in the acute stage, in this paper. The usage of (2-UPS + U)&(R + RPS)&(2-RR) hybrid mechanism and a mirror motion actuator had the advantages of compact structure, large working space and short wearing time to the HE-LRR. Then, the mobility of the HE-LRR was calculated and the motion property was analyzed based on screw theory. Meanwhile, the trajectory planning of the HE-LRR was carried out based on MOTOmed^® motion training. Finally, the motion capture and surface electromyography (sEMG) signal acquisition experiments in the MOTOmed motion training were performed. The foot trajectory experimental effect and the lower-limb muscle groups activation rules were studied ulteriorly. The experimental results showed that the HE-LRR achieved good kinematic accuracy and lower limb muscle groups training effect, illustrating that the HE-LRR possessed good application prospects for the lower-limb rehabilitation of patients in the acute stage. This research could also provide a theoretical basis for improving the standardization and compliance of lower-limb robot rehabilitation training. Full article

(This article belongs to the Special Issue Smart Machines: Applications and Advances in Human Motion Analysis)

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18 pages, 3313 KiB

Open AccessArticle

Functional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network

by Simão Carvalho, Ana Correia, Joana Figueiredo, Jorge M. Martins and Cristina P. Santos

Machines 2021, 9(11), 253; https://doi.org/10.3390/machines9110253 - 26 Oct 2021

Cited by 2 | Viewed by 3119

Abstract

Neurological diseases may reduce Tibialis Anterior (TA) muscle recruitment capacity causing gait disorders, such as drop foot (DF). The majority of DF patients still retain excitable nerves and muscles which makes Functional Electrical Stimulation (FES) an adequate technique to restore lost mobility. Recent studies suggest the need for developing personalized and assist-as-needed control strategies for wearable FES in order to promote natural and functional movements while reducing the early onset of fatigue. This study contributes to a real-time implementation of a trajectory tracking FES control strategy for personalized DF correction. This strategy combines a feedforward Non-Linear Autoregressive Neural Network with Exogenous inputs (NARXNN) with a feedback PD controller. This control strategy advances with a user-specific TA muscle model achieved by the NARXNN’s ability to model dynamic systems relying on the foot angle and angular velocity as inputs. A closed-loop, fully wearable stimulation system was achieved using an ISTim stimulator and wearable inertial sensor for electrical stimulation and user’s kinematic gait sensing, respectively. Results showed that the NARXNN architecture with 2 hidden layers and 10 neurons provided the highest performance for modelling the kinematic behaviour of the TA muscle. The proposed trajectory tracking control revealed a low discrepancy between real and reference foot trajectories (goodness of fit = 77.87%) and time-effectiveness for correctly stimulating the TA muscle towards a natural gait and DF correction. Full article

(This article belongs to the Special Issue Smart Machines: Applications and Advances in Human Motion Analysis)

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9 pages, 1853 KiB

Open AccessEditor’s ChoiceCommunication

Highly Stretchable and Kirigami-Structured Strain Sensors with Long Silver Nanowires of High Aspect Ratio

by Huiyan Huang, Catherine Jiayi Cai, Bok Seng Yeow, Jianyong Ouyang and Hongliang Ren

Machines 2021, 9(9), 186; https://doi.org/10.3390/machines9090186 - 03 Sep 2021

Cited by 5 | Viewed by 2549

Abstract

Stretchable, skin-interfaced, and wearable strain sensors have risen in recent years due to their wide-ranging potential applications in health-monitoring devices, human motion detection, and soft robots. High aspect ratio (AR) silver nanowires (AgNWs) have shown great potential in the flexible and stretchable strain sensors due to the high conductivity and flexibility of AgNW conductive networks. Hence, this work aims to fabricate highly stretchable, sensitive, and linear kirigami strain sensors with high AR AgNWs. The AgNW synthesis parameters and process windows have been identified by Taguchi’s design of experiment and analysis. Long AgNWs with a high AR of 1556 have been grown at optimized synthesis parameters using the one-pot modified polyol method. Kirigami sensors were fabricated via full encapsulation of AgNWs with Ecoflex silicon rubber. Kirigami-patterned strain sensors with long AgNWs show high stretchability, moderate sensitivity, excellent linearity (R² = 0.99) up to 70% strain and can promptly detect finger movement without obvious hysteresis. Full article

(This article belongs to the Special Issue Smart Machines: Applications and Advances in Human Motion Analysis)

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18 pages, 3424 KiB

Open AccessEditor’s ChoiceArticle

Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

by Luís Moreira, Joana Figueiredo, João Paulo Vilas-Boas and Cristina Peixoto Santos

Machines 2021, 9(8), 154; https://doi.org/10.3390/machines9080154 - 06 Aug 2021

Cited by 13 | Viewed by 3532

Abstract

Powered Assistive Devices (PADs) have been proposed to enable repetitive, user-oriented gait rehabilitation. They may include torque controllers that typically require reference joint torque trajectories to determine the most suitable level of assistance. However, a robust approach able to automatically estimate user-oriented reference joint torque trajectories, namely ankle torque, while considering the effects of varying walking speed, body mass, and height on the gait dynamics, is needed. This study evaluates the accuracy and generalization ability of two Deep Learning (DL) regressors (Long-Short Term Memory and Convolutional Neural Network (CNN)) to generate user-oriented reference ankle torque trajectories by innovatively customizing them according to the walking speed (ranging from 1.0 to 4.0 km/h) and users’ body height and mass (ranging from 1.51 to 1.83 m and 52.0 to 83.7 kg, respectively). Furthermore, this study hypothesizes that DL regressors can estimate joint torque without resourcing electromyography signals. CNN was the most robust algorithm (Normalized Root Mean Square Error: 0.70 ± 0.06; Spearman Correlation: 0.89 ± 0.03; Coefficient of Determination: 0.91 ± 0.03). No statistically significant differences were found in CNN accuracy (p-value > 0.05) whether electromyography signals are included as inputs or not, enabling a less obtrusive and accurate setup for torque estimation. Full article

(This article belongs to the Special Issue Smart Machines: Applications and Advances in Human Motion Analysis)

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