Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299372
S. Lee, M. Y. Ozsecen, Luca Della Toffola, J. Daneault, A. Puiatti, Shyamal Patel, P. Bonato
Motivated by a need for accurate assessment and monitoring of patients with knee osteoarthritis in an ambulatory setting, a wearable electrogoniometer composed of a knee angular sensor and a three-axis accelerometer placed on the thigh is developed. Accurate assessment of knee kinematics requires accurate detection of walking amongst dynamic, heterogeneous, and individualized activities of daily living. This paper investigates four different machine learning techniques for detecting occurrences of walking in uncontrolled environments based on a dataset collected from a total of 4 healthy subjects. Multi-class classifier (random forest) based detection method showed the best performance, which supports 90% precision and 75% recall. The in-depth analysis and interpretation of the results show that accurate decision boundaries are necessary between 1) fast walking and descending stairs, 2) slow walking and ascending stairs, as well as 3) slow walking and transitional activities. This work provides a systematic approach to detect occurrences of walking in uncontrolled living conditions, which can also be extended to other activities.
{"title":"Activity detection in uncontrolled free-living conditions using a single accelerometer","authors":"S. Lee, M. Y. Ozsecen, Luca Della Toffola, J. Daneault, A. Puiatti, Shyamal Patel, P. Bonato","doi":"10.1109/BSN.2015.7299372","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299372","url":null,"abstract":"Motivated by a need for accurate assessment and monitoring of patients with knee osteoarthritis in an ambulatory setting, a wearable electrogoniometer composed of a knee angular sensor and a three-axis accelerometer placed on the thigh is developed. Accurate assessment of knee kinematics requires accurate detection of walking amongst dynamic, heterogeneous, and individualized activities of daily living. This paper investigates four different machine learning techniques for detecting occurrences of walking in uncontrolled environments based on a dataset collected from a total of 4 healthy subjects. Multi-class classifier (random forest) based detection method showed the best performance, which supports 90% precision and 75% recall. The in-depth analysis and interpretation of the results show that accurate decision boundaries are necessary between 1) fast walking and descending stairs, 2) slow walking and ascending stairs, as well as 3) slow walking and transitional activities. This work provides a systematic approach to detect occurrences of walking in uncontrolled living conditions, which can also be extended to other activities.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127701121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299374
Lang Wang, Zhipei Huang, Jiankang Wu, Yu Meng, R. Ding
Quantitative measures of autonomic regulation of cardiovascular system have clinical and prognostic value in a variety of cardiovascular diseases. This paper proposes a model-based method in the measurement of baroreflex sensitivity, sympathetic and parasympathetic activity. The method measures the continuous blood pressure and heart rate in orthostatic scenario, models the baroreflex and sympathetic regulation process, solves for personalized model parameters by optimization using measured blood pressure and heart rate variations. Experimental results have shown the validation of the quantitative measures and the effectiveness of the method.
{"title":"A model-based method to evaluate autonomic regulation of cardiovascular system","authors":"Lang Wang, Zhipei Huang, Jiankang Wu, Yu Meng, R. Ding","doi":"10.1109/BSN.2015.7299374","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299374","url":null,"abstract":"Quantitative measures of autonomic regulation of cardiovascular system have clinical and prognostic value in a variety of cardiovascular diseases. This paper proposes a model-based method in the measurement of baroreflex sensitivity, sympathetic and parasympathetic activity. The method measures the continuous blood pressure and heart rate in orthostatic scenario, models the baroreflex and sympathetic regulation process, solves for personalized model parameters by optimization using measured blood pressure and heart rate variations. Experimental results have shown the validation of the quantitative measures and the effectiveness of the method.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126477126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299404
Edmond Mitchell, A. Ahmadi, N. O’Connor, C. Richter, Evan Farrell, Jennifer Kavanagh, Kieran Moran
Human motion analysis technologies have been widely employed to identify injury determining factors and provide objective and quantitative feedback to athletes to help prevent injury. However, most of these technologies are: expensive, restricted to laboratory environments, and can require significant post processing. This reduces their ecological validity, adoption and usefulness. In this paper, we present a novel wearable inertial sensor framework to accurately distinguish between symmetrical and asymmetrical running patterns in an unconstrained environment. The framework can automatically classify symmetry/asymmetry using Short Time Fourier Transform (STFT) and other time domain features in conjunction with a customized Random Forest classifier. The accuracy of the designed framework is up to 94% using 3-D accelerometer and 3-D gyroscope data from a sensor node attached on the upper back of a subject. The upper back inertial sensors data were then down-sampled by a factor of 4 to simulate utilizing low-cost inertial sensors whilst also facilitating a decrease of the computational cost to achieve near real-time application. We conclude that the proposed framework can potentially pave the way for employing low-cost sensors, such as those used in smartphones, attached on the upper back to provide injury related and performance feedback in real-time in unconstrained environments.
{"title":"Automatically detecting asymmetric running using time and frequency domain features","authors":"Edmond Mitchell, A. Ahmadi, N. O’Connor, C. Richter, Evan Farrell, Jennifer Kavanagh, Kieran Moran","doi":"10.1109/BSN.2015.7299404","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299404","url":null,"abstract":"Human motion analysis technologies have been widely employed to identify injury determining factors and provide objective and quantitative feedback to athletes to help prevent injury. However, most of these technologies are: expensive, restricted to laboratory environments, and can require significant post processing. This reduces their ecological validity, adoption and usefulness. In this paper, we present a novel wearable inertial sensor framework to accurately distinguish between symmetrical and asymmetrical running patterns in an unconstrained environment. The framework can automatically classify symmetry/asymmetry using Short Time Fourier Transform (STFT) and other time domain features in conjunction with a customized Random Forest classifier. The accuracy of the designed framework is up to 94% using 3-D accelerometer and 3-D gyroscope data from a sensor node attached on the upper back of a subject. The upper back inertial sensors data were then down-sampled by a factor of 4 to simulate utilizing low-cost inertial sensors whilst also facilitating a decrease of the computational cost to achieve near real-time application. We conclude that the proposed framework can potentially pave the way for employing low-cost sensors, such as those used in smartphones, attached on the upper back to provide injury related and performance feedback in real-time in unconstrained environments.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126557613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299376
B. Groh, Tobias Cibis, R. O. Schill, B. Eskofier
A simple method for an underwater pose determination of scuba divers can provide a deeper insight in the biomechanics of scuba diving and thereby improve education and training systems. In this work, we present an inertial sensor-based approach for the pose determination of the upper body and the shank orientation during fin kicks. Accelerometer measurements of gravity and a gyroscope-based method are used to determine absolute body angles in reference to the ground and the angular change of the shanks during fin kicks. The proposed algorithms were evaluated with data acquired from ten divers and a camera-based gold standard. The results were analyzed to a mean error of 0° with a standard deviation of 10° for the upper body pose determination. The absolute angle of the shanks at the turning points between fin kicks was determined with an error of 0° ± 11°, the relative shank angle with an error of 0° ± 8°.
{"title":"IMU-based pose determination of scuba divers' bodies and shanks","authors":"B. Groh, Tobias Cibis, R. O. Schill, B. Eskofier","doi":"10.1109/BSN.2015.7299376","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299376","url":null,"abstract":"A simple method for an underwater pose determination of scuba divers can provide a deeper insight in the biomechanics of scuba diving and thereby improve education and training systems. In this work, we present an inertial sensor-based approach for the pose determination of the upper body and the shank orientation during fin kicks. Accelerometer measurements of gravity and a gyroscope-based method are used to determine absolute body angles in reference to the ground and the angular change of the shanks during fin kicks. The proposed algorithms were evaluated with data acquired from ten divers and a camera-based gold standard. The results were analyzed to a mean error of 0° with a standard deviation of 10° for the upper body pose determination. The absolute angle of the shanks at the turning points between fin kicks was determined with an error of 0° ± 11°, the relative shank angle with an error of 0° ± 8°.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125151966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299368
Michael Hardegger, Benjamin Ledergerber, S. Mutter, C. Vogt, J. Seiter, Alberto Calatroni, G. Tröster
Sensor technology that is unobtrusively integrated into the clothing and equipment of an athlete can support the training of sport activities and monitor the athlete's progress. In this paper, we propose two wearable systems that support ice hockey players in the training of skating and shooting. These assistants measure the motions of players and compare them with reference executions of the same activities by professional players. A third system that we introduce monitors the player;s activities during a hockey game and creates a match report for objective performance measurement. For each of the three proposed applications, we present a prototype setup that we evaluate with amateur and professional players. The main findings are i) that with a skate-worn motion sensor and user-dependent training, eight skating motions can be spotted with an accuracy above 90%, ii) that stick-integrated sensors enable the measurement of relevant shot features, which differentiate professional from amateur athletes, and iii) that it is possible to spot important ice hockey activities in the signals of body-worn motion sensors worn during a game.
{"title":"Sensor technology for ice hockey and skating","authors":"Michael Hardegger, Benjamin Ledergerber, S. Mutter, C. Vogt, J. Seiter, Alberto Calatroni, G. Tröster","doi":"10.1109/BSN.2015.7299368","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299368","url":null,"abstract":"Sensor technology that is unobtrusively integrated into the clothing and equipment of an athlete can support the training of sport activities and monitor the athlete's progress. In this paper, we propose two wearable systems that support ice hockey players in the training of skating and shooting. These assistants measure the motions of players and compare them with reference executions of the same activities by professional players. A third system that we introduce monitors the player;s activities during a hockey game and creates a match report for objective performance measurement. For each of the three proposed applications, we present a prototype setup that we evaluate with amateur and professional players. The main findings are i) that with a skate-worn motion sensor and user-dependent training, eight skating motions can be spotted with an accuracy above 90%, ii) that stick-integrated sensors enable the measurement of relevant shot features, which differentiate professional from amateur athletes, and iii) that it is possible to spot important ice hockey activities in the signals of body-worn motion sensors worn during a game.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130737067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299398
Morris Vanegas, L. Stirling
Accurate estimations of variability in multiple donnings of sensor suites may aid algorithm development for wearable motion capture systems that make use of Inertial Measurement Units (IMUs). The accuracy of any algorithm incorporating these sensors is limited by the accuracy of the sensor to segment calibration. When either sensor placement (use by a non-expert) or limb motion during calibration (natural human variation) vary, the estimations are affected. In this study, 22 participants self-placed IMUs on three locations and performed six prescribed motions during each of these five donnings. For absolute placement of the sensors, the chest location mean was less than the forearm, which was less than the bicep. For sensor orientation, the opposite ordering of location was found. No difference in sensor rotation was found between the bicep and forearm, but both locations differed from the chest location. Results were analyzed at the beginning of prescribed motions.
{"title":"Characterization of inertial measurement unit placement on the human body upon repeated donnings","authors":"Morris Vanegas, L. Stirling","doi":"10.1109/BSN.2015.7299398","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299398","url":null,"abstract":"Accurate estimations of variability in multiple donnings of sensor suites may aid algorithm development for wearable motion capture systems that make use of Inertial Measurement Units (IMUs). The accuracy of any algorithm incorporating these sensors is limited by the accuracy of the sensor to segment calibration. When either sensor placement (use by a non-expert) or limb motion during calibration (natural human variation) vary, the estimations are affected. In this study, 22 participants self-placed IMUs on three locations and performed six prescribed motions during each of these five donnings. For absolute placement of the sensors, the chest location mean was less than the forearm, which was less than the bicep. For sensor orientation, the opposite ordering of location was found. No difference in sensor rotation was found between the bicep and forearm, but both locations differed from the chest location. Results were analyzed at the beginning of prescribed motions.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131773221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299413
P. Joosen, V. Exadaktylos, D. Berckmans
Car racing is an intense sport that requires high and constant mental engagement. As the average age of race car drivers increases, it becomes more apparent that the mental aspect of the sport is becoming more important. In this work, a Body Sensor Network system was developed consisting of a Heart Rate monitor, an external GPS sensor and two smartphones. Experiments were conducted during the last race of the VLN series at the Nürburgring in Germany. The Heart Rate of the driver was combined with the 3D accelerometer of the mobile phone using an existing algorithm that is calculating the stress level of the driver in real-time. The stress level, along with GPS information is subsequently transmitted via the mobile phone network and the crew is able to see the position and the stress level of the driver in real-time. Post analysis of the data indicates that there is a correlation between the stress level of the driver and specific events. Weak correlations exist (between 22% and 53%) between the stress level of the driver during an event and their performance. Finally, the difference of the stress profiles among the drivers is shown.
{"title":"An investigation on mental stress-profiling of race car drivers during a race","authors":"P. Joosen, V. Exadaktylos, D. Berckmans","doi":"10.1109/BSN.2015.7299413","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299413","url":null,"abstract":"Car racing is an intense sport that requires high and constant mental engagement. As the average age of race car drivers increases, it becomes more apparent that the mental aspect of the sport is becoming more important. In this work, a Body Sensor Network system was developed consisting of a Heart Rate monitor, an external GPS sensor and two smartphones. Experiments were conducted during the last race of the VLN series at the Nürburgring in Germany. The Heart Rate of the driver was combined with the 3D accelerometer of the mobile phone using an existing algorithm that is calculating the stress level of the driver in real-time. The stress level, along with GPS information is subsequently transmitted via the mobile phone network and the crew is able to see the position and the stress level of the driver in real-time. Post analysis of the data indicates that there is a correlation between the stress level of the driver and specific events. Weak correlations exist (between 22% and 53%) between the stress level of the driver during an event and their performance. Finally, the difference of the stress profiles among the drivers is shown.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115091060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299394
Rajesh Kuni, Yashaswini Prathivadi, Jian Wu, Terrell R. Bennett, R. Jafari
Context aware systems like smart homes and offices will benefit from determining human-object and human-human interactions. In this paper, we explore interaction detection methods using only wearable Inertial Measurement Units (IMUs). The interactions we explore involve two actors - the primary person and a secondary object or person. We explore how several commonly used time domain signal processing operators can be utilized to detect the similar movements in the interactions and thus the interactions themselves. We also utilize a well-known boosting algorithm to potentially increase the accuracy of the operator results. The techniques operate on the magnitudes of the acceleration and gyroscope readings to keep the analysis independent of the orientation of the sensors. The detection accuracy for six interactions using the approach presented in the paper range from 84.2% to 69.6%.
{"title":"Exploration of interactions detectable by wearable IMU sensors","authors":"Rajesh Kuni, Yashaswini Prathivadi, Jian Wu, Terrell R. Bennett, R. Jafari","doi":"10.1109/BSN.2015.7299394","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299394","url":null,"abstract":"Context aware systems like smart homes and offices will benefit from determining human-object and human-human interactions. In this paper, we explore interaction detection methods using only wearable Inertial Measurement Units (IMUs). The interactions we explore involve two actors - the primary person and a secondary object or person. We explore how several commonly used time domain signal processing operators can be utilized to detect the similar movements in the interactions and thus the interactions themselves. We also utilize a well-known boosting algorithm to potentially increase the accuracy of the operator results. The techniques operate on the magnitudes of the acceleration and gyroscope readings to keep the analysis independent of the orientation of the sensors. The detection accuracy for six interactions using the approach presented in the paper range from 84.2% to 69.6%.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121207319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299392
A. Tobola, F. Streit, Chris Espig, Oliver Korpok, Christian Sauter, N. Lang, Björn Schmitz, Christian Hofmann, M. Struck, C. Weigand, Heike Leutheuser, B. Eskofier, Georg Fischer
Long battery runtime is one of the most wanted properties of wearable sensor systems. The sampling rate has an high impact on the power consumption. However, defining a sufficient sampling rate, especially for cutting edge mobile sensors is difficult. Often, a high sampling rate, up to four times higher than necessary, is chosen as a precaution. Especially for biomedical sensor applications many contradictory recommendations exist, how to select the appropriate sample rate. They all are motivated from one point of view - the signal quality. In this paper we motivate to keep the sampling rate as low as possible. Therefore we reviewed common algorithms for biomedical signal processing. For each algorithm the number of operations depending on the data rate has been estimated. The Bachmann-Landau notation has been used to evaluate the computational complexity in dependency of the sampling rate. We found linear, logarithmic, quadratic and cubic dependencies.
{"title":"Sampling rate impact on energy consumption of biomedical signal processing systems","authors":"A. Tobola, F. Streit, Chris Espig, Oliver Korpok, Christian Sauter, N. Lang, Björn Schmitz, Christian Hofmann, M. Struck, C. Weigand, Heike Leutheuser, B. Eskofier, Georg Fischer","doi":"10.1109/BSN.2015.7299392","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299392","url":null,"abstract":"Long battery runtime is one of the most wanted properties of wearable sensor systems. The sampling rate has an high impact on the power consumption. However, defining a sufficient sampling rate, especially for cutting edge mobile sensors is difficult. Often, a high sampling rate, up to four times higher than necessary, is chosen as a precaution. Especially for biomedical sensor applications many contradictory recommendations exist, how to select the appropriate sample rate. They all are motivated from one point of view - the signal quality. In this paper we motivate to keep the sampling rate as low as possible. Therefore we reviewed common algorithms for biomedical signal processing. For each algorithm the number of operations depending on the data rate has been estimated. The Bachmann-Landau notation has been used to evaluate the computational complexity in dependency of the sampling rate. We found linear, logarithmic, quadratic and cubic dependencies.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116722535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299415
Shaad Mahmud, Honggang Wang, Yong K Kim, Dapeng Li
A miniaturized monopole antenna was designed and fabricated on an organic paper and LCP material for wireless body area network. Compared with previous work, the proposed design has 20% reduction of the antenna size but with enhanced performance. The effects of the compact coplanar antenna under different twisting conditions is described in this paper. The proposed antennas are simulated and designed on an organic paper and a Liquid Crystal Polymer (LCP) substrate with dielectric constant Dr= 3.4 and thickness 15μm and 5μm respectively, occupying the area of 22×30mm2. A detailed discussion about radiation pattern, Gain, antenna efficiency and power pattern is given with the help of experimental and numerical results.
{"title":"Development of an inkjet printed green antenna and twisting effect for wireless body area network","authors":"Shaad Mahmud, Honggang Wang, Yong K Kim, Dapeng Li","doi":"10.1109/BSN.2015.7299415","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299415","url":null,"abstract":"A miniaturized monopole antenna was designed and fabricated on an organic paper and LCP material for wireless body area network. Compared with previous work, the proposed design has 20% reduction of the antenna size but with enhanced performance. The effects of the compact coplanar antenna under different twisting conditions is described in this paper. The proposed antennas are simulated and designed on an organic paper and a Liquid Crystal Polymer (LCP) substrate with dielectric constant Dr= 3.4 and thickness 15μm and 5μm respectively, occupying the area of 22×30mm2. A detailed discussion about radiation pattern, Gain, antenna efficiency and power pattern is given with the help of experimental and numerical results.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115942549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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