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Liu X. Intelligent Physical Training Data Processing Based on Wearable Devices. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1207457. [PMID: 35634051 PMCID: PMC9142307 DOI: 10.1155/2022/1207457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 04/27/2022] [Accepted: 05/03/2022] [Indexed: 11/17/2022]
Abstract
Intelligent processing of physical training data based on wearable devices is conducive to improving the efficiency and rationality of physical training. The current data processing methods cannot effectively extract the features contained in the data, resulting in low accuracy in tasks such as classification. This paper proposes an intelligent processing method for sports training data based on statistical methods and deep learning methods. First, the original data are preprocessed by some statistical methods to obtain the original feature vector. Then, the autoencoder model is used to extract the high-level hidden features in the original data. Finally, we input the extracted feature vector into a designed convolutional neural network classification model and generate the final classification result. Evaluation on the open Human Activity Recognition Using Smartphones Dataset shows that our proposed method achieves the best results compared with current methods.
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Affiliation(s)
- Xuguang Liu
- Nanjing University of Information Science and Technology, Nanjing 210044, China
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2
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Chen J, Li G, Liang H, Zhao S, Sun J, Qin M. An amplitude-based characteristic parameter extraction algorithm for cerebral edema detection based on electromagnetic induction. Biomed Eng Online 2021; 20:74. [PMID: 34344370 PMCID: PMC8335876 DOI: 10.1186/s12938-021-00913-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 07/26/2021] [Indexed: 11/10/2022] Open
Abstract
Background Cerebral edema is a common condition secondary to any type of neurological injury. The early diagnosis and monitoring of cerebral edema is of great importance to improve the prognosis. In this article, a flexible conformal electromagnetic two-coil sensor was employed as the electromagnetic induction sensor, associated with a vector network analyzer (VNA) for signal generation and receiving. Measurement of amplitude data over the frequency range of 1–100 MHz is conducted to evaluate the changes in cerebral edema. We proposed an Amplitude-based Characteristic Parameter Extraction (Ab-CPE) algorithm for multi-frequency characteristic analysis over the frequency range of 1–100 MHz and investigated its performance in electromagnetic induction-based cerebral edema detection and distinction of its acute/chronic phase. Fourteen rabbits were enrolled to establish cerebral edema model and the 24 h real-time monitoring experiments were carried out for algorithm verification. Results The proposed Ab-CPE algorithm was able to detect cerebral edema with a sensitivity of 94.1% and specificity of 95.4%. Also, in the early stage, it can detect cerebral edema with a sensitivity of 85.0% and specificity of 87.5%. Moreover, the Ab-CPE algorithm was able to distinguish between acute and chronic phase of cerebral edema with a sensitivity of 85.0% and specificity of 91.0%. Conclusion The proposed Ab-CPE algorithm is suitable for multi-frequency characteristic analysis. Combined with this algorithm, the electromagnetic induction method has an excellent performance on the detection and monitoring of cerebral edema.
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Affiliation(s)
- Jingbo Chen
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Gen Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China.
| | - Huayou Liang
- China Aerodynamics Research and Development Center Low Speed Aerodynamic Institute, Mianyang, Sichuan, China
| | - Shuanglin Zhao
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jian Sun
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Mingxin Qin
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China.
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Chen J, Li G, Chen M, Jin G, Zhao S, Bai Z, Yang J, Liang H, Xu J, Sun J, Qin M. A noninvasive flexible conformal sensor for accurate real-time monitoring of local cerebral edema based on electromagnetic induction. PeerJ 2020; 8:e10079. [PMID: 33083136 PMCID: PMC7546241 DOI: 10.7717/peerj.10079] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 09/11/2020] [Indexed: 12/24/2022] Open
Abstract
Cerebral edema (CE) is a non-specific pathological swelling of the brain secondary to any type of neurological injury. The real-time monitoring of focal CE mostly found in early stage is of great significance to reduce mortality and disability. Magnetic Induction Phase Shift (MIPS) is expected to achieve non-invasive continuous monitoring of CE. However, most existing MIPS sensors are made of hard materials which makes it difficult to accurately retrieve CE information. In this article, we designed a conformal two-coil structure and a single-coil structure, and studied their sensitivity map using finite element method (FEM). After that, the conformal MIPS sensor that is preferable for local CE monitoring was fabricated by flexible printed circuit (FPC). Next, physical experiments were conducted to investigate its performance on different levels of simulated CE solution volume, measurement distance, and bending. Subsequently, 14 rabbits were chosen to establish CE model and another three rabbits were selected as controls. The 24-hour MIPS real-time monitoring experiments was carried out to verify that the feasibility. Results showed a gentler attenuation trend of the conformal two-coil structure, compared with the single-coil structure. In addition, the novel flexible conformal MIPS sensor has a characteristic of being robust to bending according to the physical experiments. The results of animal experiments showed that the sensor can be used for CE monitoring. It can be concluded that this flexible conformal MIPS sensor is desirable for local focusing measurement of CE and subsequent multidimensional information extraction for predicting model. Also, it enables a much more comfortable environment for long-time bedside monitoring.
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Affiliation(s)
- Jingbo Chen
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Gen Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Mingsheng Chen
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Gui Jin
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Shuanglin Zhao
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Zelin Bai
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jun Yang
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Huayou Liang
- China Aerodynamics Research and Development Center Low Speed Aerodynamic Institute, Mianyang, China
| | - Jia Xu
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jian Sun
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China.,Department of Neurosurgery, Southwest Hospital, Chongqing, China
| | - Mingxin Qin
- College of Biomedical Engineering, Third Military Medical University (Army Medical University), Chongqing, China
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Tsao L, Nussbaum MA, Kim S, Ma L. Modelling performance during repetitive precision tasks using wearable sensors: a data-driven approach. ERGONOMICS 2020; 63:831-849. [PMID: 32321375 DOI: 10.1080/00140139.2020.1759700] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 04/13/2020] [Indexed: 06/11/2023]
Abstract
In modern manufacturing systems, especially assembly lines, human input is a critical resource to provide dexterity and flexibility. However, the repetitive precision tasks common in assembly lines can have adverse effects on workers and overall system performance. We present a data-driven approach to evaluating task performance using wearable sensor data (kinematics, electromyography and heart rate). Eighteen participants (gender-balanced) completed repeated cycles of maze tracking and assembly/disassembly. Various combinations of input data types and classification algorithms were used to model task performance. The use of the linear discriminant analysis (LDA) algorithm and kinematic data provided the most promising classification performance. The highest model accuracy was found using the LDA algorithm and all data types, with respective levels of 62.4, 88.6, 85.8 and 94.1% for predicting maze errors, maze speed, assembly/disassembly errors and assembly/disassembly speed. The presented approach provides the possibility for real-time, on-line and comprehensive monitoring of system performance in assembly-lines or similar industries. Practitioner summary: This paper proposed models the repetitive precision task performance using data collected from wearable sensors. The use of the LDA algorithm and kinematic data provided the most promising classification performance. The presented approach provides the possibility for real-time, on-line and comprehensive monitoring of system performance in assembly lines or similar industries. Abbreviations: AD: anterior deltoid; BB: biceps brachii; ECR: extensor carpi radialis; ECU: extensor carpi ulnaris; FCR: flexor carpi radialis; FCU: flexor carpi ulnaris; FN: false negatives; FP: false positives; HR: heart rate; HRR: heart rate reserve; IMUs: inertial measurement units; kNN: k-nearest neighbors; LDA: linear discriminant analysis; MD: medial deltoid; MF: median power frequency; MNF: mean power frequency; MVIC: maximum voluntary isometric contraction; nRMS: normalized root-mean-square amplitudes; PD: posterior deltoid; RandFor: random forests; RHR: resting heart rate; RMS: root-mean-square amplitudes; sEMG: surface electromyographic; SVM: support vector machines; TB: triceps brachii medial; TN: true negatives; TP: true positives; t-SNE: t-distributed Stochastic Neighbor Embedding; UT: upper trapezius.
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Affiliation(s)
- Liuxing Tsao
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA
- Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Maury A Nussbaum
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Sunwook Kim
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Liang Ma
- Department of Industrial Engineering, Tsinghua University, Beijing, China
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Park EB, Heo JC, Lee JH. Novel smart clothing with dry electrode biosensor for real-time automatic diagnosis of cardiovascular diseases. Biomed Mater Eng 2018; 29:587-599. [PMID: 30400073 DOI: 10.3233/bme-181010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The incidence of heart disease increases with age. The typical method of monitoring arrhythmia is to use a body patch type sensor with a wet electrode. Even though this sensor is easy to use, it has several disadvantages such as problems caused by wet electrodes in tissues when they are monitored for long periods. Thus, a monitoring sensor integrated into clothes with a dry electrode is proposed. In this study, we developed a smart outdoor shirt equipped with a dry electrode electrocardiogram (ECG) sensor for a cardiac arrhythmia computer-aided diagnosis system. The sensor can be inserted in a console close to the chest, charged, used to communicate wirelessly, and can be connected to a smartphone application. According to experiments, the ECG signals measured by the smart shirt indicated that 97.5 ± 1% of the signals could be measured in an immobile state and at least 85.2 ± 2% of the signals could be measured during movement. In addition, we propose a computer-aided diagnosis system for detecting cardiac arrhythmia. It was determined through experiments that the system can detect arrhythmia with an accuracy of 98.2 ± 2%.
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Affiliation(s)
- Eun-Bin Park
- Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, South Korea
| | - Jin-Chul Heo
- Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, South Korea
| | - Jong-Ha Lee
- Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, South Korea
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Park JA, Han HJ, Heo JC, Lee JH. Computer aided diagnosis sensor integrated outdoor shirts for real time heart disease monitoring. Comput Assist Surg (Abingdon) 2017; 22:176-185. [DOI: 10.1080/24699322.2017.1389396] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- Ji-Ae Park
- Dept. of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, Republic of Korea
| | - Hee-Jeong Han
- School of Visual Arts, Fashion Design Major, Keimyung University, Daegu, Republic of Korea
| | - Jin-Chul Heo
- Dept. of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, Republic of Korea
| | - Jong-Ha Lee
- Dept. of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, Republic of Korea
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Shen CL, Huang TH, Hsu PC, Ko YC, Chen FL, Wang WC, Kao T, Chan CT. Respiratory Rate Estimation by Using ECG, Impedance, and Motion Sensing in Smart Clothing. J Med Biol Eng 2017; 37:826-842. [PMID: 30220900 PMCID: PMC6132375 DOI: 10.1007/s40846-017-0247-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 10/12/2016] [Indexed: 11/29/2022]
Abstract
The needs for light-weight and soft smart clothing in homecare have been rising since the past decade. Many smart textile sensors have been developed and applied to automatic physiological and user-centered environmental status recognition. In the present study, we propose wearable multi-sensor smart clothing for homecare monitoring based on an economic fabric electrode with high elasticity and low resistance. The wearable smart clothing integrated with heterogeneous sensors is capable to measure multiple human biosignals (ECG and respiration), acceleration, and gyro information. Five independent respiratory signals (electric impedance plethysmography, respiratory induced frequency variation, respiratory induced amplitude variation, respiratory induced intensity variation, and respiratory induced movement variation) are obtained. The smart clothing can provide accurate respiratory rate estimation by using three different techniques (Naïve Bayes inference, static Kalman filter, and dynamic Kalman filter). During the static sitting experiments, respiratory induced frequency variation has the best performance; whereas during the running experiments, respiratory induced amplitude variation has the best performance. The Naïve Bayes inference and dynamic Kalman filter have shown good results. The novel smart clothing is soft, elastic, and washable and it is suitable for long-term monitoring in homecare medical service and healthcare industry.
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Affiliation(s)
- Chien-Lung Shen
- Department of Biomedical Engineering, National Yang-Ming University, No.155, Sec.2, Linong Street, Taipei, 112 Taiwan, ROC
- Taiwan Textile Research Institute, No.6, Chengtian Rd., Tucheng Dist., New Taipei City, 23674 Taiwan, ROC
| | - Tzu-Hao Huang
- Taiwan Textile Research Institute, No.6, Chengtian Rd., Tucheng Dist., New Taipei City, 23674 Taiwan, ROC
| | - Po-Chun Hsu
- Taiwan Textile Research Institute, No.6, Chengtian Rd., Tucheng Dist., New Taipei City, 23674 Taiwan, ROC
| | - Ya-Chi Ko
- Taiwan Textile Research Institute, No.6, Chengtian Rd., Tucheng Dist., New Taipei City, 23674 Taiwan, ROC
| | - Fen-Ling Chen
- Taiwan Textile Research Institute, No.6, Chengtian Rd., Tucheng Dist., New Taipei City, 23674 Taiwan, ROC
| | - Wei-Chun Wang
- Taiwan Textile Research Institute, No.6, Chengtian Rd., Tucheng Dist., New Taipei City, 23674 Taiwan, ROC
| | - Tsair Kao
- Department of Biomedical Engineering, National Yang-Ming University, No.155, Sec.2, Linong Street, Taipei, 112 Taiwan, ROC
| | - Chia-Tai Chan
- Department of Biomedical Engineering, National Yang-Ming University, No.155, Sec.2, Linong Street, Taipei, 112 Taiwan, ROC
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Kiruthiga G, Sharmila A, Mahalakshmi P, Muruganandam M. Power optimisation for wearable heart rate measurement device with wireless charging. J Med Eng Technol 2017; 41:288-297. [PMID: 28277813 DOI: 10.1080/03091902.2017.1293742] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Continuous measurement of heart rate is necessary for monitoring the patients with heart ailments. A wearable which continuously measures heart rate of an individual by a method called reflectance-based photoplethysmography (PPG) computes the heart rate of an individual according to the volumetric changes in blood flowing through the body is developed. In order to make the device more compact as well as with IP67 and IP68 standard, wireless charging technique is employed because it helps to get rid of wires while charging. Following the Qi standard for designing wireless power receiver circuits makes the device interoperable and work with greater efficiency with reduced losses. Impedance matching and designing the circuit to operate under resonance condition increases coupling efficiency in case of inductive coupling.
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Affiliation(s)
- G Kiruthiga
- a School of Electrical Engineering (SELECT) , VIT University , Vellore , India
| | - A Sharmila
- a School of Electrical Engineering (SELECT) , VIT University , Vellore , India
| | - P Mahalakshmi
- a School of Electrical Engineering (SELECT) , VIT University , Vellore , India
| | - M Muruganandam
- b Department of Electrical and Computer Engineering , Wollega University , Oromia , Ethiopia
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9
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Teichmann D, Teichmann M, Weitz P, Wolfart S, Leonhardt S, Walter M. SensInDenT-Noncontact Sensors Integrated Into Dental Treatment Units. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2017; 11:225-233. [PMID: 27448369 DOI: 10.1109/tbcas.2016.2574922] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper presents the first system design (SensInDenT) for noncontact cardiorespiratory monitoring during dental treatment. The system is integrated into a dental treatment unit, and combines sensors based on electromagnetic, optical, and mechanical coupling at different sensor locations. The measurement principles and circuits are described and a system overview is presented. Furthermore, a first proof of concept is provided by taking measurements in healthy volunteers under laboratory conditions.
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Sacchi L, Holmes JH. Progress in Biomedical Knowledge Discovery: A 25-year Retrospective. Yearb Med Inform 2016; Suppl 1:S117-29. [PMID: 27488403 PMCID: PMC5171499 DOI: 10.15265/iys-2016-s033] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVES We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. METHODS We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. RESULTS A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992- 2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. CONCLUSIONS Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains. Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.
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Affiliation(s)
| | - J H Holmes
- John H Holmes, Institute for Biomedical Informatics, University of Pennsylvania School of Medicine, 717 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA, Tel: 215-898-4833, Fax: 215-573-5325, E-Mail:
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Melnykowycz M, Tschudin M, Clemens F. Piezoresistive Soft Condensed Matter Sensor for Body-Mounted Vital Function Applications. SENSORS 2016; 16:s16030326. [PMID: 26959025 PMCID: PMC4813901 DOI: 10.3390/s16030326] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Revised: 02/17/2016] [Accepted: 02/19/2016] [Indexed: 02/05/2023]
Abstract
A soft condensed matter sensor (SCMS) designed to measure strains on the human body is presented. The hybrid material based on carbon black (CB) and a thermoplastic elastomer (TPE) was bonded to a textile elastic band and used as a sensor on the human wrist to measure hand motion by detecting the movement of tendons in the wrist. Additionally it was able to track the blood pulse wave of a person, allowing for the determination of pulse wave peaks corresponding to the systole and diastole blood pressures in order to calculate the heart rate. Sensor characterization was done using mechanical cycle testing, and the band sensor achieved a gauge factor of 4–6.3 while displaying low signal relaxation when held at a strain levels. Near-linear signal performance was displayed when loading to successively higher strain levels up to 50% strain.
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Affiliation(s)
- Mark Melnykowycz
- Laboratory for High Performance Ceramics, Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, Dübendorf 8600, Switzerland.
| | - Michael Tschudin
- STBL Medical Research AG, Höh-Rohnenweg 6, Wilen 8832, Switzerland.
| | - Frank Clemens
- Laboratory for High Performance Ceramics, Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, Dübendorf 8600, Switzerland.
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Moorman R, Ruffle J. Martin Black award for the best paper published in 2013. Physiol Meas 2014; 35:1927-8. [PMID: 25229317 DOI: 10.1088/0967-3334/35/10/1927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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13
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Foussier J, Teichmann D, Jia J, Misgeld B, Leonhardt S. An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors. BMC Med Inform Decis Mak 2014; 14:37. [PMID: 24886253 PMCID: PMC4029942 DOI: 10.1186/1472-6947-14-37] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Accepted: 04/29/2014] [Indexed: 11/22/2022] Open
Abstract
Background Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. Methods We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Results Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min −1 (0.3 min −1) and -0.7 bpm (1.7 bpm) (compared to -0.2 min −1 (0.4 min −1) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time. Conclusions It is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals.
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Affiliation(s)
- Jerome Foussier
- Philips Chair for Medical Information Technology, Aachen University, Pauwelsstraße 20, 52074 Aachen, Germany.
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Teichmann D, Kuhn A, Leonhardt S, Walter M. The MAIN Shirt: a textile-integrated magnetic induction sensor array. SENSORS 2014; 14:1039-56. [PMID: 24412900 PMCID: PMC3926601 DOI: 10.3390/s140101039] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2013] [Revised: 12/20/2013] [Accepted: 12/23/2013] [Indexed: 11/21/2022]
Abstract
A system is presented for long-term monitoring of respiration and pulse. It comprises four non-contact sensors based on magnetic eddy current induction that are textile-integrated into a shirt. The sensors are technically characterized by laboratory experiments that investigate the sensitivity and measuring depth, as well as the mutual interaction between adjacent pairs of sensors. The ability of the device to monitor respiration and pulse is demonstrated by measurements in healthy volunteers. The proposed system (called the MAIN (magnetic induction) Shirt) does not need electrodes or any other skin contact. It is wearable, unobtrusive and can easily be integrated into an individual's daily routine. Therefore, the system appears to be a suitable option for long-term monitoring in a domestic environment or any other unsupervised telemonitoring scenario.
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Affiliation(s)
- Daniel Teichmann
- Philips Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, Aachen 52074, Germany.
| | - Andreas Kuhn
- Philips Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, Aachen 52074, Germany.
| | - Steffen Leonhardt
- Philips Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, Aachen 52074, Germany.
| | - Marian Walter
- Philips Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, Aachen 52074, Germany.
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