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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8057

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Dr. Sen Qiu

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Guest Editor

School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
Interests: information fusion; motion tracking; body sensor networks; pattern recognition

Special Issue Information

Dear Colleagues,

Thanks to technological advances in the field of motion monitoring, new potential applications for motion sensor are emerging, such as in Internet of Things, virtual reality, artificial intelligence, wearable computing, smart buildings, intelligent sports, wisdom medical system, and digital twin for structural health monitoring.

In general, all activities that involve motion might benefit from motion sensor systems. They are increasingly being used for human motion capture and recognition given their unobtrusiveness, light weight, low cost, and amenability to ambulatory use without interfering with the user’s daily life. Meanwhile, motion sensors can be qualified for structural health inspection due to their suitability for long-term use in any location. These key features have made motion sensors suitable for both indoor and outdoor ambulatory applications and, hence, they are applicable in both consumer and industrial scenarios. The effectiveness of motion analysis via motion sensor-based systems depends on the robustness of both sensors and data collection protocols.

In this context, this Special Issue aims to connect researchers in the field of emerging motion sensor-based systems for motion tracking applications, focusing on sensor fusion, in order to share ideas and conceptual approaches and to discuss the recent advances in this field, addressing innovative solutions and emerging issues. Research detailing the fusion methods of multimodality and multilocation sensors are encouraged.

We will accept submission of full-length research articles and reviews focused on this research topic. Topics of interest include, but are not limited to, the following:

Topics of Interest

Motion sensor data fusion;
Wearable computing;
Human activity recognition;
Smart homes and buildings;
Motion capture;
Digital twin;
Innovative applications of wearable sensor systems;
Advanced motion sensor signal processing;
Motion sensors and related techniques for motor diagnosis;
Motion sensors and related techniques for structural health inspection.

Dr. Sen Qiu
Guest Editor

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Keywords

motion tracking
motion sensor applications
sensor data fusion

Published Papers (5 papers)

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Research

21 pages, 5695 KiB

Open AccessArticle

Utilizing Motion Capture Systems for Instrumenting the OCRA Index: A Study on Risk Classification for Upper Limb Work-Related Activities

by Pablo Aqueveque, Guisella Peña, Manuel Gutiérrez, Britam Gómez, Enrique Germany, Gustavo Retamal and Paulina Ortega-Bastidas

Sensors 2023, 23(17), 7623; https://doi.org/10.3390/s23177623 - 2 Sep 2023

Viewed by 857

Abstract

In the search to enhance ergonomic risk assessments for upper limb work-related activities, this study introduced and validated the efficiency of an inertial motion capture system, paired with a specialized platform that digitalized the OCRA index. Conducted in a semi-controlled environment, the proposed methodology was compared to traditional risk classification techniques using both inertial and optical motion capture systems. The inertial method encompassed 18 units in a Bluetooth Low Energy tree topology network for activity recording, subsequently analyzed for risk using the platform. Principal outcomes emphasized the optical system’s preeminence, aligning closely with the conventional technique. The optical system’s superiority was further evident in its alignment with the traditional method. Meanwhile, the inertial system followed closely, with an error margin of just ±0.098 compared to the optical system. Risk classification was consistent across all systems. The inertial system demonstrated strong performance metrics, achieving F1-scores of 0.97 and 1 for “risk” and “no risk” classifications, respectively. Its distinct advantage of portability was reinforced by participants’ feedback on its user-friendliness. The results highlight the inertial system’s potential, mirroring the precision of both traditional and optical methods and achieving a 65% reduction in risk assessment time. This advancement mitigates the need for intricate video setups, emphasizing its potential in ergonomic assessments. Full article

(This article belongs to the Special Issue Motion Sensor)

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15 pages, 2730 KiB

Open AccessArticle

RFID Backscatter Based Sport Motion Sensing Using ECOC-Based SVM

by Lei Han and Xia Hua

Sensors 2023, 23(17), 7324; https://doi.org/10.3390/s23177324 - 22 Aug 2023

Viewed by 731

Abstract

With the advent of the 5G era, radio frequency identification (RFID) has been widely applied in various fields as one of the key technologies for the Internet of Things (IoT) to realize the Internet of Everything (IoE). In recent years, RFID-based motion sensing has emerged as an important research area with great potential for development. In this paper, an RFID backscatter sport motion sensing scheme is proposed, which effectively solves the multi-classification problem by using the received signal strength (RSS) of the backscattered RFID and the error correcting output coding (ECOC)-based support vector machine (SVM). We conduct extensive experiments to validate the effectiveness of the proposed scheme, in which the signal intensities of different types of action poses are collected and the SVM is used as the classification algorithm to achieve high classification accuracies. Full article

(This article belongs to the Special Issue Motion Sensor)

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20 pages, 8947 KiB

Open AccessArticle

Dataglove for Sign Language Recognition of People with Hearing and Speech Impairment via Wearable Inertial Sensors

by Ang Ji, Yongzhen Wang, Xin Miao, Tianqi Fan, Bo Ru, Long Liu, Ruicheng Nie and Sen Qiu

Sensors 2023, 23(15), 6693; https://doi.org/10.3390/s23156693 - 26 Jul 2023

Viewed by 2214

Abstract

Finding ways to enable seamless communication between deaf and able-bodied individuals has been a challenging and pressing issue. This paper proposes a solution to this problem by designing a low-cost data glove that utilizes multiple inertial sensors with the purpose of achieving efficient and accurate sign language recognition. In this study, four machine learning models—decision tree (DT), support vector machine (SVM), K-nearest neighbor method (KNN), and random forest (RF)—were employed to recognize 20 different types of dynamic sign language data used by deaf individuals. Additionally, a proposed attention-based mechanism of long and short-term memory neural networks (Attention-BiLSTM) was utilized in the process. Furthermore, this study verifies the impact of the number and position of data glove nodes on the accuracy of recognizing complex dynamic sign language. Finally, the proposed method is compared with existing state-of-the-art algorithms using nine public datasets. The results indicate that both the Attention-BiLSTM and RF algorithms have the highest performance in recognizing the twenty dynamic sign language gestures, with an accuracy of 98.85% and 97.58%, respectively. This provides evidence for the feasibility of our proposed data glove and recognition methods. This study may serve as a valuable reference for the development of wearable sign language recognition devices and promote easier communication between deaf and able-bodied individuals. Full article

(This article belongs to the Special Issue Motion Sensor)

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17 pages, 2882 KiB

Open AccessArticle

A Multi-Label Based Physical Activity Recognition via Cascade Classifier

by Lingfei Mo, Yaojie Zhu and Lujie Zeng

Sensors 2023, 23(5), 2593; https://doi.org/10.3390/s23052593 - 26 Feb 2023

Cited by 3 | Viewed by 1551

Abstract

Physical activity recognition is a field that infers human activities used in machine learning techniques through wearable devices and embedded inertial sensors of smartphones. It has gained much research significance and promising prospects in the fields of medical rehabilitation and fitness management. Generally, datasets with different wearable sensors and activity labels are used to train machine learning models, and most research has achieved satisfactory performance for these datasets. However, most of the methods are incapable of recognizing the complex physical activity of free living. To address the issue, we propose a cascade classifier structure for sensor-based physical activity recognition from a multi-dimensional perspective, with two types of labels that work together to represent an exact type of activity. This approach employed the cascade classifier structure based on a multi-label system (Cascade Classifier on Multi-label, CCM). The labels reflecting the activity intensity would be classified first. Then, the data flow is divided into the corresponding activity type classifier according to the output of the pre-layer prediction. The dataset of 110 participants has been collected for the experiment on PA recognition. Compared with the typical machine learning algorithms of Random Forest (RF), Sequential Minimal Optimization (SMO) and K Nearest Neighbors (KNN), the proposed method greatly improves the overall recognition accuracy of ten physical activities. The results show that the RF-CCM classifier has achieved 93.94% higher accuracy than the 87.93% obtained from the non-CCM system, which could obtain better generalization performance. The comparison results reveal that the novel CCM system proposed is more effective and stable in physical activity recognition than the conventional classification methods. Full article

(This article belongs to the Special Issue Motion Sensor)

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20 pages, 9920 KiB

Open AccessArticle

Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor

by Yisa Zhang, Yuchen Zhao, Hengyi Lv, Yang Feng, Hailong Liu and Chengshan Han

Sensors 2022, 22(7), 2614; https://doi.org/10.3390/s22072614 - 29 Mar 2022

Cited by 3 | Viewed by 1690

Abstract

The dynamic vision sensor (DVS) measures asynchronously change of brightness per pixel, then outputs an asynchronous and discrete stream of spatiotemporal event information that encodes the time, location, and sign of brightness changes. The dynamic vision sensor has outstanding properties compared to sensors of traditional cameras, with very high dynamic range, high temporal resolution, low power consumption, and does not suffer from motion blur. Hence, dynamic vision sensors have considerable potential for computer vision in scenarios that are challenging for traditional cameras. However, the spatiotemporal event stream has low visualization and is incompatible with existing image processing algorithms. In order to solve this problem, this paper proposes a new adaptive slicing method for the spatiotemporal event stream. The resulting slices of the spatiotemporal event stream contain complete object information, with no motion blur. The slices can be processed either with event-based algorithms or by constructing slices into virtual frames and processing them with traditional image processing algorithms. We tested our slicing method using public as well as our own data sets. The difference between the object information entropy of the slice and the ideal object information entropy is less than 1%. Full article

(This article belongs to the Special Issue Motion Sensor)

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