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Wei H, Ye J, Li J, Wang Y. Design and Simulation of a Hierarchical Parallel Distributed Processing Model for Orientation Selection Based on Primary Visual Cortex. Biomimetics (Basel) 2023; 8:314. [PMID: 37504201 PMCID: PMC10807402 DOI: 10.3390/biomimetics8030314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023] Open
Abstract
The study of the human visual system not only helps to understand the mechanism of the visual system but also helps to develop visual aid systems to help the visually impaired. As the systematic study of neural signal processing mechanisms in early biological vision continues, the hierarchical structure of the visual system is gradually being dissected, bringing the possibility of building brain-like computational models from a bionic perspective. In this paper, we follow the objective facts of neurobiology and propose a parallel distributed processing computational model of primary visual cortex orientation selection with reference to the complex process of visual signal processing and transmission between the retina to the primary visual cortex, the hierarchical receptive field structure between cells in each layer, and the very fine-grained parallel distributed characteristics of cortical visual computation, which allow for high speed and efficiency. We approach the design from a brain-like chip perspective, map our network model on the field programmable gate array (FPGA), and perform simulation experiments. The results verify the possibility of implementing our proposed model with programmable devices, which can be applied to small wearable devices with low power consumption and low latency.
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Affiliation(s)
- Hui Wei
- Laboratory of Algorithms for Cognitive Models, School of Computer Science, Fudan University, Shanghai 200082, China
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2
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Abstract
This paper presents a comprehensive review of the wearable healthcare monitoring systems proposed by the researchers to date. One of the earliest wearable recorders, named “a silicon locket for ECG monitoring”, was developed at the Indian Institute of Technology, Bombay, in 2003. Thus, the wearable health monitoring systems, started with the acquisition of a single signal/ parameter to the present generation smart and affordable multi-parameter recording/monitoring systems, have evolved manifolds in these two decades. Wearable systems have dramatically changed in terms of size, cost, functionality, and accuracy. The early-day wearable recorders were with limited functionalities against today’s systems, e.g., Apple’s iWatch which comprises abundant health monitoring features like heart rate monitoring, breathing app, accelerometers, smart walking/ activity monitoring, and alerts. Most of the present-day smartphones are not only capable of recording various health features like body temperature, heart rate, photoplethysmograph (PPG) signal, calory consumption, smart activity monitoring, stress measurement, etc. through different apps, but they also help the user to get monitored by a family physician via GSM or even internet of things (IoT). One of the latest, state-of-the-art real-time personal health monitoring systems, Wearable IoT-cloud-based health monitoring system (WISE), is a beautiful amalgamation of body area sensor network (BASN) and IoT framework for ubiquitous health monitoring. The future of wearable health monitoring systems will be far beyond the IoT and BASN.
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Chen CH, Wang CC, Chen YZ. Intelligent Brushing Monitoring Using a Smart Toothbrush with Recurrent Probabilistic Neural Network. SENSORS 2021; 21:s21041238. [PMID: 33578684 PMCID: PMC7916355 DOI: 10.3390/s21041238] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/10/2021] [Accepted: 01/12/2021] [Indexed: 11/16/2022]
Abstract
Smart toothbrushes equipped with inertial sensors are emerging as high-tech oral health products in personalized health care. The real-time signal processing of nine-axis inertial sensing and toothbrush posture recognition requires high computational resources. This paper proposes a recurrent probabilistic neural network (RPNN) for toothbrush posture recognition that demonstrates the advantages of low computational resources as a requirement, along with high recognition accuracy and efficiency. The RPNN model is trained for toothbrush posture recognition and brushing position and then monitors the correctness and integrity of the Bass Brushing Technique. Compared to conventional deep learning models, the recognition accuracy of RPNN is 99.08% in our experiments, which is 16.2% higher than that of the Convolutional Neural Network (CNN) and 21.21% higher than the Long Short-Term Memory (LSTM) model. The model we used can greatly reduce the computing power of hardware devices, and thus, our system can be used directly on smartphones.
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Vavrinsky E, Subjak J, Donoval M, Wagner A, Zavodnik T, Svobodova H. Application of Modern Multi-Sensor Holter in Diagnosis and Treatment. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2663. [PMID: 32392697 PMCID: PMC7273207 DOI: 10.3390/s20092663] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 05/04/2020] [Accepted: 05/04/2020] [Indexed: 12/11/2022]
Abstract
Modern Holter devices are very trendy tools used in medicine, research, or sport. They monitor a variety of human physiological or pathophysiological signals. Nowadays, Holter devices have been developing very fast. New innovative products come to the market every day. They have become smaller, smarter, cheaper, have ultra-low power consumption, do not limit everyday life, and allow comfortable measurements of humans to be accomplished in a familiar and natural environment, without extreme fear from doctors. People can be informed about their health and 24/7 monitoring can sometimes easily detect specific diseases, which are normally passed during routine ambulance operation. However, there is a problem with the reliability, quality, and quantity of the collected data. In normal life, there may be a loss of signal recording, abnormal growth of artifacts, etc. At this point, there is a need for multiple sensors capturing single variables in parallel by different sensing methods to complement these methods and diminish the level of artifacts. We can also sense multiple different signals that are complementary and give us a coherent picture. In this article, we describe actual interesting multi-sensor principles on the grounds of our own long-year experiences and many experiments.
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Affiliation(s)
- Erik Vavrinsky
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (J.S.); (M.D.); (T.Z.)
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia
| | - Jan Subjak
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (J.S.); (M.D.); (T.Z.)
| | - Martin Donoval
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (J.S.); (M.D.); (T.Z.)
| | - Alexandra Wagner
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia; (A.W.); (H.S.)
| | - Tomas Zavodnik
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (J.S.); (M.D.); (T.Z.)
| | - Helena Svobodova
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia; (A.W.); (H.S.)
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Bazi Y, Al Rahhal MM, AlHichri H, Ammour N, Alajlan N, Zuair M. Real-Time Mobile-Based Electrocardiogram System for Remote Monitoring of Patients with Cardiac Arrhythmias. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001420580136] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this study, we propose an electrocardiogram (ECG) system for the simultaneous and remote monitoring of multiple heart patients. It consists of three main components: patient, sever, and monitoring units. The patient unit uses a wearable miniature sensor that continuously measures ECG signals and sends them to a smart mobile phone via a Bluetooth connection. In the mobile device, the ECG signals can be stored, displayed on screen, and automatically transmitted to a distant server unit over the internet; the server stores ECG data from several patients. Health care stakeholders use a monitoring unit to retrieve the ECG signals of multiple patients at any time from the server for display and real-time automatic analysis. The analysis includes segmentation of the ECG signal into separate heartbeats followed by arrhythmia detection and classification. When compared to existing real-time ECG systems, where the detection of abnormalities is usually performed using simple rules, the proposed system implements a real-time classification module that is based on a support vector machine (SVM) classifier. Extensive experimental results on ECG data obtained from a TechPatientTM simulator, a real person, and 20 records from the MIT arrhythmia database are reported and discussed.
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Affiliation(s)
- Yakoub Bazi
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Mohamad M. Al Rahhal
- Information Science Department, College of Applied Computer Science, King Saud University, Riyadh 11543, Saudi Arabia
| | - Haikel AlHichri
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Nassim Ammour
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Naif Alajlan
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Mansour Zuair
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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Panwar M, Biswas D, Bajaj H, Jobges M, Turk R, Maharatna K, Acharyya A. Rehab-Net: Deep Learning Framework for Arm Movement Classification Using Wearable Sensors for Stroke Rehabilitation. IEEE Trans Biomed Eng 2019; 66:3026-3037. [DOI: 10.1109/tbme.2019.2899927] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Ku Abd Rahim KN, Elamvazuthi I, Izhar LI, Capi G. Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors. SENSORS (BASEL, SWITZERLAND) 2018; 18:E4132. [PMID: 30486242 PMCID: PMC6308488 DOI: 10.3390/s18124132] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 11/11/2018] [Accepted: 11/19/2018] [Indexed: 01/08/2023]
Abstract
Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.
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Affiliation(s)
- Ku Nurhanim Ku Abd Rahim
- Smart Assistive and Rehabilitative Technology (SMART) Research Group, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Malaysia.
| | - I Elamvazuthi
- Smart Assistive and Rehabilitative Technology (SMART) Research Group, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Malaysia.
| | - Lila Iznita Izhar
- Smart Assistive and Rehabilitative Technology (SMART) Research Group, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Malaysia.
| | - Genci Capi
- Assistive Robotics Laboratory, Department of Mechanical Engineering, Faculty of Science and Engineering, HOSEI University, Tokyo 184-8584, Japan.
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Senthil Kumar K, Amutha R, Palanivelan M, Gururaj D, Richard Jebasingh S, Anitha Mary M, Anitha S, Savitha V, Priyanka R, Balachandran A, Adithya H, Shaji A, C. A. Receive diversity based transmission data rate optimization for improved network lifetime and delay efficiency of Wireless Body Area Networks. PLoS One 2018; 13:e0206027. [PMID: 30359405 PMCID: PMC6201903 DOI: 10.1371/journal.pone.0206027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 10/05/2018] [Indexed: 11/18/2022] Open
Abstract
Wireless Body Area Network (WBAN) has become the emerging technology due to its ability to provide intelligent and cost-effective healthcare monitoring solution. The biological sensors used in WBAN are energy-constrained and required to be functional for a longer duration. Also, the sensed data should be communicated in reasonable time. Therefore, network lifetime and delay have become the primary concerns in the design of WBAN. In this paper, Receive Diversity based Transmission Data Rate Optimization (RDTDRO) scheme is proposed to improve the network lifetime and delay efficiency of Multi level-Quadrature Amplitude Modulation (M-QAM) based WBAN. In the proposed RDTDRO scheme, minimum energy consumption is ensured by optimizing the transmission data rate with respect to a given transmission distance and number of receive antennas while satisfying the Bit Error Rate (BER) requirements. The performance of proposed RDTDRO is analyzed in terms of network lifetime and delay difference and is compared with conventional Baseline and Rate optimized schemes. The results show that at a transmission distance of 0.3 m, the proposed RDTDRO scheme with a receive diversity order of 4 achieves 1.30 times and 1.27 times improvement in network lifetime over conventional Baseline and Rate optimized schemes respectively. From the results, it is also evident that at a transmission distance of 0.3 m, the proposed RDTDRO scheme with a receive diversity order of 4 is delay efficient as it achieves delay difference of 0.75 μs and 0.29 μs over conventional Baseline and Rate optimized schemes respectively.
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Affiliation(s)
- K. Senthil Kumar
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
- * E-mail:
| | - R. Amutha
- Department of Electronics and Communication Engineering, SSN College of Engineering, Chennai, India
| | - M. Palanivelan
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
| | - D. Gururaj
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
| | - S. Richard Jebasingh
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
| | - M. Anitha Mary
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
| | - S. Anitha
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
| | - V. Savitha
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
| | - R. Priyanka
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
| | - Amruth Balachandran
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
| | - H. Adithya
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
| | - Asher Shaji
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
| | - Anchana C.
- Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India
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Analysis of In-to-Out Wireless Body Area Network Systems: Towards QoS-Aware Health Internet of Things Applications. ELECTRONICS 2016. [DOI: 10.3390/electronics5030038] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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Altini M, Casale P, Penders J, Amft O. Cardiorespiratory fitness estimation in free-living using wearable sensors. Artif Intell Med 2016; 68:37-46. [PMID: 26948954 DOI: 10.1016/j.artmed.2016.02.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Revised: 02/16/2016] [Accepted: 02/16/2016] [Indexed: 01/01/2023]
Abstract
OBJECTIVE In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data. METHODS Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living. RESULTS We show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants. CONCLUSIONS Our investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.
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Affiliation(s)
- Marco Altini
- Signal Processing and Systems, Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands; Bloom Technologies, Agoralaan Building Abis 2.13, 3590 Diepenbeek, Belgium.
| | - Pierluigi Casale
- imec The Netherlands, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands
| | - Julien Penders
- imec The Netherlands, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands
| | - Oliver Amft
- Chair of Sensor Technology, University of Passau, Innstrasse 41, 94032 Passau, Germany
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11
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Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application. MICROMACHINES 2015. [DOI: 10.3390/mi6081100] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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12
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Gao L, Bourke A, Nelson J. Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Med Eng Phys 2014; 36:779-85. [DOI: 10.1016/j.medengphy.2014.02.012] [Citation(s) in RCA: 114] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2012] [Revised: 01/08/2014] [Accepted: 02/08/2014] [Indexed: 12/20/2022]
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Zhao J, Huang JX, Hu X. BPLT+: A Bayesian-based personalized recommendation model for health care. BMC Genomics 2013; 14 Suppl 4:S6. [PMID: 24266984 PMCID: PMC3849459 DOI: 10.1186/1471-2164-14-s4-s6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation (BPLT+) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate BPLT+ model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed BPLT+ model.
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Dong B, Montoye A, Moore R, Pfeiffer K, Biswas S. Energy-aware Activity Classification using Wearable Sensor Networks. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2013; 8723:87230Y. [PMID: 25075266 DOI: 10.1117/12.2018134] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
This paper presents implementation details, system characterization, and the performance of a wearable sensor network that was designed for human activity analysis. Specific machine learning mechanisms are implemented for recognizing a target set of activities with both out-of-body and on-body processing arrangements. Impacts of energy consumption by the on-body sensors are analyzed in terms of activity detection accuracy for out-of-body processing. Impacts of limited processing abilities for the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the sensor units. Impacts of varying number of sensors in terms of activity classification accuracy are also evaluated. Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body wearable sensors.
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Affiliation(s)
- Bo Dong
- Department of Electrical and Computer Engineering, Michigan State University
| | | | - Rebecca Moore
- Department of Kinesiology, Michigan State University
| | | | - Subir Biswas
- Department of Electrical and Computer Engineering, Michigan State University
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15
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A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Med Biol Eng Comput 2013; 51:485-95. [DOI: 10.1007/s11517-012-1021-6] [Citation(s) in RCA: 98] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Accepted: 12/17/2012] [Indexed: 10/27/2022]
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16
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Baig MM, Gholamhosseini H. Smart health monitoring systems: an overview of design and modeling. J Med Syst 2013; 37:9898. [PMID: 23321968 DOI: 10.1007/s10916-012-9898-z] [Citation(s) in RCA: 102] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2012] [Accepted: 09/18/2012] [Indexed: 11/25/2022]
Abstract
Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.
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Affiliation(s)
- Mirza Mansoor Baig
- Department of Electrical and Electronic Engineering, School of Engineering, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand,
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17
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Dong B, Biswas S, Montoye A, Pfeiffer K. Comparing metabolic energy expenditure estimation using wearable multi-sensor network and single accelerometer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:2866-9. [PMID: 24110325 PMCID: PMC4161942 DOI: 10.1109/embc.2013.6610138] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper presents the implementation details, system architecture and performance of a wearable sensor network that was designed for human activity recognition and energy expenditure estimation. We also included ActiGraph GT3X+ as a popular single sensor solution for detailed comparison with the proposed wearable sensor network. Linear regression and Artificial Neural Network are implemented and tested. Through a rigorous system study and experiment, it is shown that the wearable multi-sensor network outperforms the single sensor solution in terms of energy expenditure estimation.
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Affiliation(s)
- Bo Dong
- Department of Electrical and Computer Engineering, Michigan State University
| | - Subir Biswas
- Department of Electrical and Computer Engineering, Michigan State University
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18
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Dobkin BH, Dorsch A. The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil Neural Repair 2012; 25:788-98. [PMID: 21989632 DOI: 10.1177/1545968311425908] [Citation(s) in RCA: 222] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Mobile health tools that enable clinicians and researchers to monitor the type, quantity, and quality of everyday activities of patients and trial participants have long been needed to improve daily care, design more clinically meaningful randomized trials of interventions, and establish cost-effective, evidence-based practices. Inexpensive, unobtrusive wireless sensors, including accelerometers, gyroscopes, and pressure-sensitive textiles, combined with Internet-based communications and machine-learning algorithms trained to recognize upper- and lower-extremity movements, have begun to fulfill this need. Continuous data from ankle triaxial accelerometers, for example, can be transmitted from the home and community via WiFi or a smartphone to a remote data analysis server. Reports can include the walking speed and duration of every bout of ambulation, spatiotemporal symmetries between the legs, and the type, duration, and energy used during exercise. For daily care, this readily accessible flow of real-world information allows clinicians to monitor the amount and quality of exercise for risk factor management and compliance in the practice of skills. Feedback may motivate better self-management as well as serve home-based rehabilitation efforts. Monitoring patients with chronic diseases and after hospitalization or the start of new medications for a decline in daily activity may help detect medical complications before rehospitalization becomes necessary. For clinical trials, repeated laboratory-quality assessments of key activities in the community, rather than by clinic testing, self-report, and ordinal scales, may reduce the cost and burden of travel, improve recruitment and retention, and capture more reliable, valid, and responsive ratio-scaled outcome measures that are not mere surrogates for changes in daily impairment, disability, and functioning.
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Affiliation(s)
- Bruce H Dobkin
- Department of Neurology, Geffen UCLA School of Medicine, Los Angeles, CA, USA.
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Au LK, Bui AAT, Batalin MA, Xu X, Kaiser WJ. CARER: efficient dynamic sensing for continuous activity monitoring. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:2228-32. [PMID: 22254783 DOI: 10.1109/iembs.2011.6090422] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Advancement in wireless health sensor systems has triggered rapidly expanding research in continuous activity monitoring for chronic disease management or promotion and assessment of physical rehabilitation. Wireless motion sensing is increasingly important in treatments where remote collection of sensor measurements can provide an in-field objective evaluation of physical activity patterns. The well-known challenge of limited operating lifetime of energy-constrained wireless health sensor systems continues to present a primary limitation for these applications. This paper introduces CARER, a software system that supports a novel algorithm that exploits knowledge of context and dynamically schedules sensor measurement episodes within an energy consumption budget while ensuring classification accuracy. The sensor selection algorithm in the CARER system is based on Partially Observable Markov Decision Process (POMDP). The parameters for the POMDP algorithm can be obtained through standard maximum likelihood estimation. Sensor data are also collected from multiple locations of the subjects body, providing estimation of an individual's daily activity patterns.
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Affiliation(s)
- Lawrence K Au
- Electrical Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA
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Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil 2012; 9:21. [PMID: 22520559 PMCID: PMC3354997 DOI: 10.1186/1743-0003-9-21] [Citation(s) in RCA: 661] [Impact Index Per Article: 55.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2011] [Accepted: 04/20/2012] [Indexed: 12/15/2022] Open
Abstract
The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.
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Affiliation(s)
- Shyamal Patel
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA, USA
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Hyung Park
- Rehabilitation Medicine Department Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Paolo Bonato
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Leighton Chan
- Rehabilitation Medicine Department Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Mary Rodgers
- Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, MD, USA
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
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Au LK, Bui AAT, Batalin MA, Kaiser WJ. Energy-efficient context classification with dynamic sensor control. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2012; 6:167-178. [PMID: 23852981 PMCID: PMC5019960 DOI: 10.1109/tbcas.2011.2166073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Energy efficiency has been a longstanding design challenge for wearable sensor systems. It is especially crucial in continuous subject state monitoring due to the ongoing need for compact sizes and better sensors. This paper presents an energy-efficient classification algorithm, based on partially observable Markov decision process (POMDP). In every time step, POMDP dynamically selects sensors for classification via a sensor selection policy. The sensor selection problem is formalized as an optimization problem, where the objective is to minimize misclassification cost given some energy budget. State transitions are modeled as a hidden Markov model (HMM), and the corresponding sensor selection policy is represented using a finite-state controller (FSC). To evaluate this framework, sensor data were collected from multiple subjects in their free-living conditions. Relative accuracies and energy reductions from the proposed method are compared against naïve Bayes (always-on) and simple random strategies to validate the relative performance of the algorithm. When the objective is to maintain the same classification accuracy, significant energy reduction is achieved.
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Dong B, Biswas S. Wearable Networked Sensing for Human Mobility and Activity Analytics: A Systems Study. INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS AND NETWORKS : [PROCEEDINGS]. INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS AND NETWORKS 2012; 2012:1-6. [PMID: 25530911 DOI: 10.1109/comsnets.2012.6151376] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper presents implementation details, system characterization, and the performance of a wearable sensor network that was designed for human activity analysis. Specific machine learning mechanisms are implemented for recognizing a target set of activities with both out-of-body and on-body processing arrangements. Impacts of energy consumption by the on-body sensors are analyzed in terms of activity detection accuracy for out-of-body processing. Impacts of limited processing abilities in the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the sensor units. Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body wearable sensors.
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Affiliation(s)
- Bo Dong
- Electrical and Computer Engineering, Michigan State University, East Lansing, MI
| | - Subir Biswas
- Electrical and Computer Engineering, Michigan State University, East Lansing, MI
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Dobkin BH, Xu X, Batalin M, Thomas S, Kaiser W. Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke. Stroke 2011; 42:2246-50. [PMID: 21636815 DOI: 10.1161/strokeaha.110.611095] [Citation(s) in RCA: 88] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Outcome measures of mobility for large stroke trials are limited to timed walks for short distances in a laboratory, step counters and ordinal scales of disability and quality of life. Continuous monitoring and outcome measurements of the type and quantity of activity in the community would provide direct data about daily performance, including compliance with exercise and skills practice during routine care and clinical trials. METHODS Twelve adults with impaired ambulation from hemiparetic stroke and 6 healthy controls wore triaxial accelerometers on their ankles. Walking speed for repeated outdoor walks was determined by machine-learning algorithms and compared to a stopwatch calculation of speed for distances not known to the algorithm. The reliability of recognizing walking, exercise, and cycling by the algorithms was compared to activity logs. RESULTS A high correlation was found between stopwatch-measured outdoor walking speed and algorithm-calculated speed (Pearson coefficient, 0.98; P=0.001) and for repeated measures of algorithm-derived walking speed (P=0.01). Bouts of walking >5 steps, variations in walking speed, cycling, stair climbing, and leg exercises were correctly identified during a day in the community. Compared to healthy subjects, those with stroke were, as expected, more sedentary and slower, and their gait revealed high paretic-to-unaffected leg swing ratios. CONCLUSIONS Test-retest reliability and concurrent and construct validity are high for activity pattern-recognition Bayesian algorithms developed from inertial sensors. This ratio scale data can provide real-world monitoring and outcome measurements of lower extremity activities and walking speed for stroke and rehabilitation studies.
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Affiliation(s)
- Bruce H Dobkin
- Department of Neurology, School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
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Fraile JA, Bajo J, Corchado JM, Abraham A. Applying wearable solutions in dependent environments. ACTA ACUST UNITED AC 2010; 14:1459-67. [DOI: 10.1109/titb.2010.2053849] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Fu Y, Ayyagari D, Colquitt N. Pulmonary disease management system with distributed wearable sensors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2009:773-776. [PMID: 19963731 DOI: 10.1109/iembs.2009.5332741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
A pulmonary disease management system with on-body and near-body sensors is introduced in this presentation. The system is wearable for continuous ambulatory monitoring. Distributed sensor data is transferred through a wireless body area network (BAN) to a central controller for real time analysis. Physiological and environmental parameters are monitored and analyzed using prevailing clinical guidelines for self-management of environmentally-linked pulmonary ailments. The system provides patients with reminders, warnings, and instructions to reduce emergency room and physician visits, and improve clinical outcomes.
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Affiliation(s)
- Yongji Fu
- Sharp Laboratories of America, Camas, WA 98607 USA
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Lukowicz P. Wearable computing and artificial intelligence for healthcare applications. Artif Intell Med 2008; 42:95-8. [PMID: 18242968 DOI: 10.1016/j.artmed.2007.12.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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