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Kumar V, Alam MN, Park SS. Review of Recent Progress on Silicone Rubber Composites for Multifunctional Sensor Systems. Polymers (Basel) 2024; 16:1841. [PMID: 39000697 PMCID: PMC11244113 DOI: 10.3390/polym16131841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/17/2024] Open
Abstract
The latest progress (the year 2021-2024) on multifunctional sensors based on silicone rubber is reported. These multifunctional sensors are useful for real-time monitoring through relative resistance, relative current change, and relative capacitance types. The present review contains a brief overview and literature survey on the sensors and their multifunctionalities. This contains an introduction to the different functionalities of these sensors. Following the introduction, the survey on the types of filler or rubber and their fabrication are briefly described. The coming section deals with the fabrication methodology of these composites where the sensors are integrated. The special focus on mechanical and electro-mechanical properties is discussed. Electro-mechanical properties with a special focus on response time, linearity, and gauge factor are reported. The next section of this review reports the filler dispersion and its role in influencing the properties and applications of these sensors. Finally, various types of sensors are briefly reported. These sensors are useful for monitoring human body motions, breathing activity, environment or breathing humidity, organic gas sensing, and, finally, smart textiles. Ultimately, the study summarizes the key takeaway from this review article. These conclusions are focused on the merits and demerits of the sensors and are followed by their future prospects.
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Affiliation(s)
- Vineet Kumar
- School of Mechanical Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan 38541, Gyeongbuk, Republic of Korea
| | - Md Najib Alam
- School of Mechanical Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan 38541, Gyeongbuk, Republic of Korea
| | - Sang Shin Park
- School of Mechanical Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan 38541, Gyeongbuk, Republic of Korea
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2
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Suglia V, Palazzo L, Bevilacqua V, Passantino A, Pagano G, D’Addio G. A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:2199. [PMID: 38610410 PMCID: PMC11014138 DOI: 10.3390/s24072199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 02/26/2024] [Accepted: 03/08/2024] [Indexed: 04/14/2024]
Abstract
Frameworks for human activity recognition (HAR) can be applied in the clinical environment for monitoring patients' motor and functional abilities either remotely or within a rehabilitation program. Deep Learning (DL) models can be exploited to perform HAR by means of raw data, thus avoiding time-demanding feature engineering operations. Most works targeting HAR with DL-based architectures have tested the workflow performance on data related to a separate execution of the tasks. Hence, a paucity in the literature has been found with regard to frameworks aimed at recognizing continuously executed motor actions. In this article, the authors present the design, development, and testing of a DL-based workflow targeting continuous human activity recognition (CHAR). The model was trained on the data recorded from ten healthy subjects and tested on eight different subjects. Despite the limited sample size, the authors claim the capability of the proposed framework to accurately classify motor actions within a feasible time, thus making it potentially useful in a clinical scenario.
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Affiliation(s)
- Vladimiro Suglia
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy; (V.S.); (L.P.); (V.B.)
| | - Lucia Palazzo
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy; (V.S.); (L.P.); (V.B.)
- Scientific Clinical Institutes Maugeri SPA SB IRCCS, 70124 Bari, Italy; (A.P.); (G.D.)
| | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy; (V.S.); (L.P.); (V.B.)
- Apulian Bioengineering S.R.L.,Via delle Violette 14, 70026 Modugno, Italy
| | - Andrea Passantino
- Scientific Clinical Institutes Maugeri SPA SB IRCCS, 70124 Bari, Italy; (A.P.); (G.D.)
| | - Gaetano Pagano
- Scientific Clinical Institutes Maugeri SPA SB IRCCS, 70124 Bari, Italy; (A.P.); (G.D.)
| | - Giovanni D’Addio
- Scientific Clinical Institutes Maugeri SPA SB IRCCS, 70124 Bari, Italy; (A.P.); (G.D.)
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3
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Huang X, Xue Y, Ren S, Wang F. Sensor-Based Wearable Systems for Monitoring Human Motion and Posture: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:9047. [PMID: 38005436 PMCID: PMC10675437 DOI: 10.3390/s23229047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/06/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023]
Abstract
In recent years, marked progress has been made in wearable technology for human motion and posture recognition in the areas of assisted training, medical health, VR/AR, etc. This paper systematically reviews the status quo of wearable sensing systems for human motion capture and posture recognition from three aspects, which are monitoring indicators, sensors, and system design. In particular, it summarizes the monitoring indicators closely related to human posture changes, such as trunk, joints, and limbs, and analyzes in detail the types, numbers, locations, installation methods, and advantages and disadvantages of sensors in different monitoring systems. Finally, it is concluded that future research in this area will emphasize monitoring accuracy, data security, wearing comfort, and durability. This review provides a reference for the future development of wearable sensing systems for human motion capture.
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Affiliation(s)
- Xinxin Huang
- Guangdong Modern Apparel Technology & Engineering Center, Guangdong University of Technology, Guangzhou 510075, China or (X.H.); (Y.X.); (S.R.)
- Xiayi Lixing Research Institute of Textiles and Apparel, Shangqiu 476499, China
| | - Yunan Xue
- Guangdong Modern Apparel Technology & Engineering Center, Guangdong University of Technology, Guangzhou 510075, China or (X.H.); (Y.X.); (S.R.)
| | - Shuyun Ren
- Guangdong Modern Apparel Technology & Engineering Center, Guangdong University of Technology, Guangzhou 510075, China or (X.H.); (Y.X.); (S.R.)
| | - Fei Wang
- School of Textile Materials and Engineering, Wuyi University, Jiangmen 529020, China
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Gao X, Zhang P, Peng X, Zhao J, Liu K, Miao M, Zhao P, Luo D, Li Y. Autonomous motion and control of lower limb exoskeleton rehabilitation robot. Front Bioeng Biotechnol 2023; 11:1223831. [PMID: 37520296 PMCID: PMC10375019 DOI: 10.3389/fbioe.2023.1223831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/04/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction: The lower limb exoskeleton rehabilitation robot should perform gait planning based on the patient's motor intention and training status and provide multimodal and robust control schemes in the control strategy to enhance patient participation. Methods: This paper proposes an adaptive particle swarm optimization admittance control algorithm (APSOAC), which adaptively optimizes the weights and learning factors of the PSO algorithm to avoid the problem of particle swarm falling into local optimal points. The proposed improved adaptive particle swarm algorithm adjusts the stiffness and damping parameters of the admittance control online to reduce the interaction force between the patient and the robot and adaptively plans the patient's desired gait profile. In addition, this study proposes a dual RBF neural network adaptive sliding mode controller (DRNNASMC) to track the gait profile, compensate for frictional forces and external perturbations generated in the human-robot interaction using the RBF network, calculate the required moments for each joint motor based on the lower limb exoskeleton dynamics model, and perform stability analysis based on the Lyapunov theory. Results and discussion: Finally, the efficiency of the APSOAC and DRNNASMC algorithms is demonstrated by active and passive walking experiments with three healthy subjects, respectively.
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Affiliation(s)
- Xueshan Gao
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Pengfei Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Xuefeng Peng
- China Shipbuilding Industry Corporation, No.713 Institute, Zhengzhou, Henan, China
| | - Jianbo Zhao
- China Shipbuilding Industry Corporation, No.713 Institute, Zhengzhou, Henan, China
| | - Kaiyuan Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Mingda Miao
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Peng Zhao
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Dingji Luo
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Yige Li
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
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Nouriani A, Jonason A, Jean J, McGovern R, Rajamani R. System-Identification-Based Activity Recognition Algorithms With Inertial Sensors. IEEE J Biomed Health Inform 2023; 27:3119-3128. [PMID: 37389995 DOI: 10.1109/jbhi.2023.3265856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2023]
Abstract
This paper focuses on activity recognition using a single wearable inertial measurement sensor placed on the subject's chest. The ten activities that need to be identified include lying down, standing, sitting, bending and walking, among others. The activity recognition approach is based on using and identifying a transfer function associated with each activity. The appropriate input and output signals for each transfer function are first determined based on the norms of the sensor signals excited by that specific activity. Then the transfer function is identified using training data and a Wiener filter based on the auto-correlation and cross-correlation of the output and input signals. The activity occurring in real-time is recognized by computing and comparing the input-output errors associated with all the transfer functions. The performance of the developed system is evaluated using data from a group of Parkinson's disease subjects, including data obtained in a clinical setting and data obtained through remote home monitoring. On average, the developed system provides better than 90% accuracy in identifying each activity as it occurs. Activity recognition is particularly useful for PD patients in order to monitor their level of activity, characterize their postural instability and recognize high risk-activities in real-time that could lead to falls.
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Shen T, Di Giulio I, Howard M. A Probabilistic Model of Human Activity Recognition with Loose Clothing. SENSORS (BASEL, SWITZERLAND) 2023; 23:4669. [PMID: 37430582 DOI: 10.3390/s23104669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/24/2023] [Accepted: 05/05/2023] [Indexed: 07/12/2023]
Abstract
Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve higher activity recognition accuracy than rigid-attached sensors, particularly when predicting from short time windows. This work presents a probabilistic model that explains improved responsiveness and accuracy with fabric sensing from the increased statistical distance between movements recorded. The accuracy of the comfortable fabric-attached sensor can be increased by 67% more than rigid-attached sensors when the window size is 0.5s. Simulated and real human motion capture experiments with several participants confirm the model's predictions, demonstrating that this counterintuitive effect is accurately captured.
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Affiliation(s)
- Tianchen Shen
- Centre for Robotics Research, Department of Engineeing, King's College London, London WC2R 2LS, UK
| | - Irene Di Giulio
- Centre for Human and Applied Physiological Sciences, King's College London, London SE1 1UL, UK
| | - Matthew Howard
- Centre for Robotics Research, Department of Engineeing, King's College London, London WC2R 2LS, UK
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Tahir SBUD, Dogar AB, Fatima R, Yasin A, Shafiq M, Khan JA, Assam M, Mohamed A, Attia EA. Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:6632. [PMID: 36081091 PMCID: PMC9460245 DOI: 10.3390/s22176632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/03/2022] [Accepted: 08/26/2022] [Indexed: 06/15/2023]
Abstract
Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time-frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.
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Affiliation(s)
- Sheikh Badar ud din Tahir
- Department of Software Engineering, Capital University of Science and Technology (CUST), Islamabad 44000, Pakistan
| | - Abdul Basit Dogar
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
| | - Rubia Fatima
- School of Software Engineering, Tsinghua University, Beijing 100084, China
| | - Affan Yasin
- School of Software Engineering, Tsinghua University, Beijing 100084, China
| | - Muhammad Shafiq
- School of Information Engineering, Qujing Normal University, Qujing 655011, China
| | - Javed Ali Khan
- Department of Software Engineering, University of Science and Technology, Bannu 28100, Pakistan
| | - Muhammad Assam
- Department of Software Engineering, University of Science and Technology, Bannu 28100, Pakistan
| | - Abdullah Mohamed
- Research Centre, Future University in Egypt, New Cairo 11745, Egypt
| | - El-Awady Attia
- Department of Industrial Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj 16273, Saudi Arabia
- Mechanical Engineering Department, Faculty of Engineering (Shoubra), Benha University, Cairo 11629, Egypt
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Manouchehri N, Bouguila N. A nonparametric Bayesian learning model using accelerated variational inference on multivariate Beta mixture models for medical applications. INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2022. [DOI: 10.1142/s1793351x22500039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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9
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Garcia-Moreno FM, Bermudez-Edo M, Rodríguez-García E, Pérez-Mármol JM, Garrido JL, Rodríguez-Fórtiz MJ. A machine learning approach for semi-automatic assessment of IADL dependence in older adults with wearable sensors. Int J Med Inform 2021; 157:104625. [PMID: 34763192 DOI: 10.1016/j.ijmedinf.2021.104625] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 10/20/2021] [Accepted: 10/22/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND AND OBJECTIVE The assessment of dependence in older adults currently requires a manual collection of data taken from questionnaires. This process is time consuming for the clinicians and intrudes the daily life of the elderly. This paper aims to semi-automate the acquisition and analysis of health data to assess and predict the dependence in older adults while executing one instrumental activity of daily living (IADL). METHODS In a mobile-health (m-health) scenario, we analyze whether the acquisition of data through wearables during the performance of IADLs, and with the help of machine learning techniques could replace the traditional questionnaires to evaluate dependence. To that end, we collected data from wearables, while older adults do the shopping activity. A trial supervisor (TS) labelled the different shopping stages (SS) in the collected data. We performed data pre-processing techniques over those SS and analyzed them with three machine learning algorithms: k-Nearest Neighbors (k-NN), Random Forest (RF) and Support Vector Machines (SVM). RESULTS Our results confirm that it is possible to replace the traditional questionnaires with wearable data. In particular, the best learning algorithm we tried reported an accuracy of 97% in the assessment of dependence. We tuned the hyperparameters of this algorithm and used embedded feature selection technique to get the best performance with a subset of only 10 features out of the initial 85. This model considers only features extracted from four sensors of a single wearable: accelerometer, heart rate, electrodermal activity and temperature. Although these features are not observational, our current proposal is semi-automatic, because it needs a TS labelling the SS (with a smartphone application). In the future, this labelling process could be automatic as well. CONCLUSIONS Our method can semi-automatically assess the dependence, without disturbing daily activities of elderly people. This method can save clinicians' time in the evaluation of dependence in older adults and reduce healthcare costs.
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Affiliation(s)
- Francisco M Garcia-Moreno
- Department of Software Engineering, Computer Sciences School, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, 18014 Granada, Spain.
| | - Maria Bermudez-Edo
- Department of Software Engineering, Computer Sciences School, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, 18014 Granada, Spain.
| | - Estefanía Rodríguez-García
- Department of Physiology, Faculty of Health Sciences, University of Granada, Av. de la Ilustración, 60, 18016 Granada, Spain.
| | - José Manuel Pérez-Mármol
- Department of Physiology, Faculty of Health Sciences, University of Granada, Av. de la Ilustración, 60, 18016 Granada, Spain.
| | - José Luis Garrido
- Department of Software Engineering, Computer Sciences School, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, 18014 Granada, Spain.
| | - María José Rodríguez-Fórtiz
- Department of Software Engineering, Computer Sciences School, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, 18014 Granada, Spain.
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Ramos RG, Domingo JD, Zalama E, Gómez-García-Bermejo J. Daily Human Activity Recognition Using Non-Intrusive Sensors. SENSORS 2021; 21:s21165270. [PMID: 34450709 PMCID: PMC8401661 DOI: 10.3390/s21165270] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/26/2021] [Accepted: 07/31/2021] [Indexed: 11/16/2022]
Abstract
In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.
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Affiliation(s)
- Raúl Gómez Ramos
- CARTIF Technological Center, 47151 Valladolid, Spain; (J.D.D.); (E.Z.); (J.G.-G.-B.)
- ITAP-DISA, University of Valladolid, 47002 Valladolid, Spain
- Correspondence:
| | - Jaime Duque Domingo
- CARTIF Technological Center, 47151 Valladolid, Spain; (J.D.D.); (E.Z.); (J.G.-G.-B.)
| | - Eduardo Zalama
- CARTIF Technological Center, 47151 Valladolid, Spain; (J.D.D.); (E.Z.); (J.G.-G.-B.)
- ITAP-DISA, University of Valladolid, 47002 Valladolid, Spain
| | - Jaime Gómez-García-Bermejo
- CARTIF Technological Center, 47151 Valladolid, Spain; (J.D.D.); (E.Z.); (J.G.-G.-B.)
- ITAP-DISA, University of Valladolid, 47002 Valladolid, Spain
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