Ambulatory blood pressure is critical in predicting some major cardiovascular events; therefore, cuff-less and noninvasive beat-to-beat ambulatory blood pressure measurement is of great significance. Machine-learning methods have shown the potential to derive the relationship between physiological signal features and ABP. In this paper, we apply random forest method to systematically explorer the inherent connections between photoplethysmography signal, electrocardiogram signal and ambulatory blood pressure. To archive this goal, 18 features were extracted from PPG and ECG signals. Several models with most significant features as inputs and beat-to-beat ABP as outputs were trained and tested on data from the Multi-Parameter Intelligent Monitoring in Intensive Care II database. Results indicate that compared with the common pulse transit time method, the RF method gives a better performance for one-hour continuous estimation of diastolic blood pressure and systolic blood pressure under both the Association for the Advancement of Medical Instrumentation and British Hyper-tension Society standard.
{"title":"Beat-to-beat ambulatory blood pressure estimation based on random forest","authors":"Rui He, Zhipei Huang, Lianying Ji, Jiankang Wu, Huihui Li, Zhiqiang Zhang","doi":"10.1109/BSN.2016.7516258","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516258","url":null,"abstract":"Ambulatory blood pressure is critical in predicting some major cardiovascular events; therefore, cuff-less and noninvasive beat-to-beat ambulatory blood pressure measurement is of great significance. Machine-learning methods have shown the potential to derive the relationship between physiological signal features and ABP. In this paper, we apply random forest method to systematically explorer the inherent connections between photoplethysmography signal, electrocardiogram signal and ambulatory blood pressure. To archive this goal, 18 features were extracted from PPG and ECG signals. Several models with most significant features as inputs and beat-to-beat ABP as outputs were trained and tested on data from the Multi-Parameter Intelligent Monitoring in Intensive Care II database. Results indicate that compared with the common pulse transit time method, the RF method gives a better performance for one-hour continuous estimation of diastolic blood pressure and systolic blood pressure under both the Association for the Advancement of Medical Instrumentation and British Hyper-tension Society standard.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115389810","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516245
Hailing Fu, K. Cao, R. Xu, Mohamed Aziz Bhouri, R. Martinez-Botas, Sang-Gook Kim, E. Yeatman
Body sensor networks are increasingly popular in healthcare, sports, military and security. However, the power supply from conventional batteries is a key bottleneck for the development of body condition monitoring. Energy harvesting from human motion to power wearable or implantable devices is a promising alternative. This paper presents an airflow energy harvester to harness human motion energy from footsteps. An air bladder-turbine energy harvester is designed to convert the footstep motion into electrical energy. The bladders are embedded in shoes to induce airflow from foot-strikes. The turbine is employed to generate electrical energy from airflow. The design parameters of the turbine rotor, including the blade number and the inner diameter of the blades (the diameter of the turbine shaft), were optimized using the computational fluid dynamics (CFD) method. A prototype was developed and tested with footsteps from a 65 kg person. The peak output power of the harvester was first measured for different resistive loads and showed a maximum value of 90.6 mW with a 30.4 Ω load. The harvested energy was then regulated and stored in a power management circuit. 14.8 mJ was stored in the circuit from 165 footsteps, which means 90 μJ was obtained per footstep. The regulated energy was finally used to fully power a fitness tracker which consists of a pedometer and a Bluetooth module. 7.38 mJ was consumed by the tracker per Bluetooth configuration and data transmission. The tracker operated normally with the harvester working continuously.
{"title":"Footstep energy harvesting using heel strike-induced airflow for human activity sensing","authors":"Hailing Fu, K. Cao, R. Xu, Mohamed Aziz Bhouri, R. Martinez-Botas, Sang-Gook Kim, E. Yeatman","doi":"10.1109/BSN.2016.7516245","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516245","url":null,"abstract":"Body sensor networks are increasingly popular in healthcare, sports, military and security. However, the power supply from conventional batteries is a key bottleneck for the development of body condition monitoring. Energy harvesting from human motion to power wearable or implantable devices is a promising alternative. This paper presents an airflow energy harvester to harness human motion energy from footsteps. An air bladder-turbine energy harvester is designed to convert the footstep motion into electrical energy. The bladders are embedded in shoes to induce airflow from foot-strikes. The turbine is employed to generate electrical energy from airflow. The design parameters of the turbine rotor, including the blade number and the inner diameter of the blades (the diameter of the turbine shaft), were optimized using the computational fluid dynamics (CFD) method. A prototype was developed and tested with footsteps from a 65 kg person. The peak output power of the harvester was first measured for different resistive loads and showed a maximum value of 90.6 mW with a 30.4 Ω load. The harvested energy was then regulated and stored in a power management circuit. 14.8 mJ was stored in the circuit from 165 footsteps, which means 90 μJ was obtained per footstep. The regulated energy was finally used to fully power a fitness tracker which consists of a pedometer and a Bluetooth module. 7.38 mJ was consumed by the tracker per Bluetooth configuration and data transmission. The tracker operated normally with the harvester working continuously.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123147283","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516236
Lei Chen, I. Bate
Body Sensor Networks (BSNs) are being used across a wider range of applications including healthcare ones where sensors may be attached to the body to sense certain properties including Electrocardiogram (ECG). The dependability of the systems is a key concern and is affected by the way in which it is used. For example, if the leads are loosely attached then the resulting signal will not be useful. It has been reported that the rate of such error is around 4% in the intensive care unit [8] when operating medical devices by trained professionals. The problem is made worse as the users of the systems are often not trained professionals. Some work has been performed on detecting anomalous signals. However, all of it has concentrated on anomalies caused by medical conditions (e.g arrhythmia). That is, to the best of our knowledge, no prior work has looked at anomalies caused by incorrect usage. In this paper a range of usage anomalies are defined in conjunction with a cardiologist and a lightweight algorithm is developed that achieves a high identification rate.
{"title":"Identifying usage anomalies for ECG-based sensor nodes","authors":"Lei Chen, I. Bate","doi":"10.1109/BSN.2016.7516236","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516236","url":null,"abstract":"Body Sensor Networks (BSNs) are being used across a wider range of applications including healthcare ones where sensors may be attached to the body to sense certain properties including Electrocardiogram (ECG). The dependability of the systems is a key concern and is affected by the way in which it is used. For example, if the leads are loosely attached then the resulting signal will not be useful. It has been reported that the rate of such error is around 4% in the intensive care unit [8] when operating medical devices by trained professionals. The problem is made worse as the users of the systems are often not trained professionals. Some work has been performed on detecting anomalous signals. However, all of it has concentrated on anomalies caused by medical conditions (e.g arrhythmia). That is, to the best of our knowledge, no prior work has looked at anomalies caused by incorrect usage. In this paper a range of usage anomalies are defined in conjunction with a cardiologist and a lightweight algorithm is developed that achieves a high identification rate.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129727928","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516249
R. LeMoyne, F. Heerinckx, Tanya Aranca, R. D. Jager, T. Zesiewicz, Harry J. Saal
The integration of wearable and wireless inertial body sensors with machine learning offers the capacity to diagnose neurological disorders involving gait. Clinical rating scales may be unable to offer precise measurement of gait dysfunction in Friedreich's ataxia compared to wearable body and inertial sensors. Using wireless inertial sensors mounted about the ankle joint of a person with Friedreich's ataxia, the accelerometer and gyroscope signal recordings can be wirelessly transmitted to a cloud computing resource for postprocessing, such as the development of a machine learning feature set. Machine learning can be applied to distinguish between the gait features of a person with Friedreich's ataxia and a person with healthy gait characteristics as a comparator through the application of a multilayer perceptron neural network. A considerable degree of classification accuracy for distinguishing between the gait feature set for the person with Friedreich's ataxia and healthy subject was achieved. The synthesis of wearable and wireless inertial body sensors with machine learning may offer the potential to enhance clinical diagnostic acuity and conceivably prognostic foresight.
{"title":"Wearable body and wireless inertial sensors for machine learning classification of gait for people with Friedreich's ataxia","authors":"R. LeMoyne, F. Heerinckx, Tanya Aranca, R. D. Jager, T. Zesiewicz, Harry J. Saal","doi":"10.1109/BSN.2016.7516249","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516249","url":null,"abstract":"The integration of wearable and wireless inertial body sensors with machine learning offers the capacity to diagnose neurological disorders involving gait. Clinical rating scales may be unable to offer precise measurement of gait dysfunction in Friedreich's ataxia compared to wearable body and inertial sensors. Using wireless inertial sensors mounted about the ankle joint of a person with Friedreich's ataxia, the accelerometer and gyroscope signal recordings can be wirelessly transmitted to a cloud computing resource for postprocessing, such as the development of a machine learning feature set. Machine learning can be applied to distinguish between the gait features of a person with Friedreich's ataxia and a person with healthy gait characteristics as a comparator through the application of a multilayer perceptron neural network. A considerable degree of classification accuracy for distinguishing between the gait feature set for the person with Friedreich's ataxia and healthy subject was achieved. The synthesis of wearable and wireless inertial body sensors with machine learning may offer the potential to enhance clinical diagnostic acuity and conceivably prognostic foresight.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124529173","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516247
Fei Peng, Huihui Li, Kamen Ivanov, Guoru Zhao, Fang Zhou, Wenjing Du, Lei Wang
The aim of this study was to analyse quantitatively spine angle changes of subjects suffering from low back pain (LBP) during dynamic exercises. We explored the differences in the range of spine angle based on gender, disability severity, the correlation between the spine angle range and the visual analogue scale (VAS) scores, as well as the differences in standard deviations between the healthy and LBP subjects. We recruited thirty-nine LBP subjects and thirty-seven healthy people. They were asked to perform several movements from a standing position first and then from a sitting position. The motions were forward and backward bending, left and right lateral bending, as well as left and right axial rotation, respectively. Results show that for the most movements, the means of the spine angle changes in the females were larger than those in the males. In the LBP group, we observed much smaller spine angle values than those in the healthy subjects during exercise. With the increase of VAS score, a declining trend of the spine angle change was observed. There were significant differences in the spine angle range between standing and sitting positions when performing left and right axial rotation (p=0.000, p=0.002, respectively). We observed high correlations (with a max. result of r=0.804) for most movements, executed both from a standing and sitting position. We also found a wider range of standard deviation in the LBP subjects compared to healthy subjects. These results indicate that quantitative analysis of the spine angle range could provide an objective reference of the disability level, and allow for the progress assessment during the rehabilitation of low back pain patients.
{"title":"Quantitative analysis of spine angle range of individuals with low back pain performing dynamic exercises","authors":"Fei Peng, Huihui Li, Kamen Ivanov, Guoru Zhao, Fang Zhou, Wenjing Du, Lei Wang","doi":"10.1109/BSN.2016.7516247","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516247","url":null,"abstract":"The aim of this study was to analyse quantitatively spine angle changes of subjects suffering from low back pain (LBP) during dynamic exercises. We explored the differences in the range of spine angle based on gender, disability severity, the correlation between the spine angle range and the visual analogue scale (VAS) scores, as well as the differences in standard deviations between the healthy and LBP subjects. We recruited thirty-nine LBP subjects and thirty-seven healthy people. They were asked to perform several movements from a standing position first and then from a sitting position. The motions were forward and backward bending, left and right lateral bending, as well as left and right axial rotation, respectively. Results show that for the most movements, the means of the spine angle changes in the females were larger than those in the males. In the LBP group, we observed much smaller spine angle values than those in the healthy subjects during exercise. With the increase of VAS score, a declining trend of the spine angle change was observed. There were significant differences in the spine angle range between standing and sitting positions when performing left and right axial rotation (p=0.000, p=0.002, respectively). We observed high correlations (with a max. result of r=0.804) for most movements, executed both from a standing and sitting position. We also found a wider range of standard deviation in the LBP subjects compared to healthy subjects. These results indicate that quantitative analysis of the spine angle range could provide an objective reference of the disability level, and allow for the progress assessment during the rehabilitation of low back pain patients.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"602 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116340322","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516287
Yogeswaran Umasankar, A. Jalal, Pablo J. Gonzalez, Mustahsin Chowdhury, A. Alfonso, S. Bhansali
Multimodal electrochemical method comprising open circuit potential and amperometric technique has been implemented to improve the specificity of the ethanol detection in a fuel cell sensor system. A miniaturized device with LMP91000 potentiostat and a processing unit has been constructed containing simple auto-calibration algorithm. The developed processing unit consist of a low power microcontroller (MSP430F5529LP). The sensing unit composed of a three electrode proton exchange membrane (PEM) fuel cell sensor, where Nafion is the PEM. In these studies, the signal due to interference has been eliminated with the support of algorithm and multimodal electrochemical method. The results show that the sensor can detect ethanol as low as 5ppm. The constructed device was validated by comparing it with the commercially available potentiostat, and the response was similar in both devices.
{"title":"Wearable alcohol monitoring device with auto-calibration ability for high chemical specificity","authors":"Yogeswaran Umasankar, A. Jalal, Pablo J. Gonzalez, Mustahsin Chowdhury, A. Alfonso, S. Bhansali","doi":"10.1109/BSN.2016.7516287","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516287","url":null,"abstract":"Multimodal electrochemical method comprising open circuit potential and amperometric technique has been implemented to improve the specificity of the ethanol detection in a fuel cell sensor system. A miniaturized device with LMP91000 potentiostat and a processing unit has been constructed containing simple auto-calibration algorithm. The developed processing unit consist of a low power microcontroller (MSP430F5529LP). The sensing unit composed of a three electrode proton exchange membrane (PEM) fuel cell sensor, where Nafion is the PEM. In these studies, the signal due to interference has been eliminated with the support of algorithm and multimodal electrochemical method. The results show that the sensor can detect ethanol as low as 5ppm. The constructed device was validated by comparing it with the commercially available potentiostat, and the response was similar in both devices.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121685750","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516255
Zhiqiang Zhang, Huihui Li, D. Mandic
Bioelectrical signal analysis is gaining significant interests from both academics and industries due to its capability for improved diagnosis and therapy of chronic diseases. In practice, different bio-signals, such as EEG, ECG, EOG and EMG, are usually contaminating each other, and the measured signal is the linear combination of them. It is critical to separate them since analysis of one type or several of them separately is of more interest. In the case of multichannel recording, several blind source separation methods are available to extract its original components. However, for single channel scenarios, the problem has yet to be well studied. Therefore in this paper, we explore blind source separation and artefact cancellation for a single channel signal by combining signal decomposition method singular spectrum analysis (SSA) with different blind source separation methods, such as principal component analysis (PCA), maximum noise fraction (MNF), independent component analysis (ICA) and canonical correlation analysis (CCA). We also systematically compare the separation performance by combing different decomposition methods (wavelet transform (WT), ensemble empirical mode decomposition (EEMD) and SSA) with blind source separation methods (PCA, MNF ICA and CCA). The good simulation results have demonstrated the effectiveness and efficiency of the proposed method.
{"title":"Blind source separation and artefact cancellation for single channel bioelectrical signal","authors":"Zhiqiang Zhang, Huihui Li, D. Mandic","doi":"10.1109/BSN.2016.7516255","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516255","url":null,"abstract":"Bioelectrical signal analysis is gaining significant interests from both academics and industries due to its capability for improved diagnosis and therapy of chronic diseases. In practice, different bio-signals, such as EEG, ECG, EOG and EMG, are usually contaminating each other, and the measured signal is the linear combination of them. It is critical to separate them since analysis of one type or several of them separately is of more interest. In the case of multichannel recording, several blind source separation methods are available to extract its original components. However, for single channel scenarios, the problem has yet to be well studied. Therefore in this paper, we explore blind source separation and artefact cancellation for a single channel signal by combining signal decomposition method singular spectrum analysis (SSA) with different blind source separation methods, such as principal component analysis (PCA), maximum noise fraction (MNF), independent component analysis (ICA) and canonical correlation analysis (CCA). We also systematically compare the separation performance by combing different decomposition methods (wavelet transform (WT), ensemble empirical mode decomposition (EEMD) and SSA) with blind source separation methods (PCA, MNF ICA and CCA). The good simulation results have demonstrated the effectiveness and efficiency of the proposed method.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132059351","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516226
H. Kalantarian, C. Sideris, Tuan Le, Christine E. King, M. Sarrafzadeh
Among the major challenges in the realization of practical health monitoring systems is the identification of short-duration events from larger signals. Time-series segmentation refers to the challenge of subdividing a continuous stream of data into discrete windows, which are individually processed using statistical classifiers to recognize various activities or events. In this paper, we propose a probabilistic algorithm for segmenting time-series signals, in which window boundaries are dynamically adjusted when the probability of correct classification is low. Our proposed scheme is benchmarked using an audio-based nutrition-monitoring case-study. Our evaluation shows that algorithm improves the number of correctly classified instances from a baseline of 75% to 94% using the RandomForest classifier.
{"title":"A framework for probabilistic segmentation of continuous sensor signals","authors":"H. Kalantarian, C. Sideris, Tuan Le, Christine E. King, M. Sarrafzadeh","doi":"10.1109/BSN.2016.7516226","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516226","url":null,"abstract":"Among the major challenges in the realization of practical health monitoring systems is the identification of short-duration events from larger signals. Time-series segmentation refers to the challenge of subdividing a continuous stream of data into discrete windows, which are individually processed using statistical classifiers to recognize various activities or events. In this paper, we propose a probabilistic algorithm for segmenting time-series signals, in which window boundaries are dynamically adjusted when the probability of correct classification is low. Our proposed scheme is benchmarked using an audio-based nutrition-monitoring case-study. Our evaluation shows that algorithm improves the number of correctly classified instances from a baseline of 75% to 94% using the RandomForest classifier.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"775 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133921160","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516275
D. Piette, Tomas Norton, V. Exadaktylos, D. Berckmans
When it comes to equestrian disciplines, the horse-rider dyad is amongst the most discussed topics. Recently the emergence of equitation science has led to an increased interest in objectively quantifying the interaction between the rider and horse. In this paper a methodology is presented to evaluate how the mental state of police horses interacts with that of their riders in order to assess the performance of police horses. This paper demonstrates how Body Sensor Network technology can be applied for real-time monitoring of the horse-rider dyad. The results of the study demonstrate that the mental state interaction between rider and horse is significantly different between bad police horses and good police horses.
{"title":"Real-time monitoring of the horse-rider dyad using body sensor network technology","authors":"D. Piette, Tomas Norton, V. Exadaktylos, D. Berckmans","doi":"10.1109/BSN.2016.7516275","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516275","url":null,"abstract":"When it comes to equestrian disciplines, the horse-rider dyad is amongst the most discussed topics. Recently the emergence of equitation science has led to an increased interest in objectively quantifying the interaction between the rider and horse. In this paper a methodology is presented to evaluate how the mental state of police horses interacts with that of their riders in order to assess the performance of police horses. This paper demonstrates how Body Sensor Network technology can be applied for real-time monitoring of the horse-rider dyad. The results of the study demonstrate that the mental state interaction between rider and horse is significantly different between bad police horses and good police horses.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134450566","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516248
R. Rajagopalan
Wireless body sensor networks are widely used for monitoring individuals in assisted living facilities and has emerged as a promising technology in e-healthcare. Such networks consist of sensors on the body or clothing of an individual for measuring vital signals such as heart beat, body temperature, and electrocardiogram. This enables patients to experience greater physical mobility and independence eliminating the need to stay in the hospital. Efficient and reliable transmission of data from on body sensors to medical personnel via multi-hop routing is critical for continuous health monitoring. In this paper, we propose a new routing algorithm for energy efficient routing in body sensor networks for reliable health monitoring. We model the routing problem as a constrained multi-objective optimization problem maximizing the throughput while minimizing the energy consumption subject to a constraint on end to end latency. We have designed a new constrained multi-objective genetic algorithm (CMOGA) for obtaining energy efficient routes. Simulation results show that CMOGA demonstrates the advantages of multi-objective optimization and outperforms a widely used and well known multi-objective evolutionary algorithm.
{"title":"Energy efficient routing algorithm for patient monitoring in body sensor networks","authors":"R. Rajagopalan","doi":"10.1109/BSN.2016.7516248","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516248","url":null,"abstract":"Wireless body sensor networks are widely used for monitoring individuals in assisted living facilities and has emerged as a promising technology in e-healthcare. Such networks consist of sensors on the body or clothing of an individual for measuring vital signals such as heart beat, body temperature, and electrocardiogram. This enables patients to experience greater physical mobility and independence eliminating the need to stay in the hospital. Efficient and reliable transmission of data from on body sensors to medical personnel via multi-hop routing is critical for continuous health monitoring. In this paper, we propose a new routing algorithm for energy efficient routing in body sensor networks for reliable health monitoring. We model the routing problem as a constrained multi-objective optimization problem maximizing the throughput while minimizing the energy consumption subject to a constraint on end to end latency. We have designed a new constrained multi-objective genetic algorithm (CMOGA) for obtaining energy efficient routes. Simulation results show that CMOGA demonstrates the advantages of multi-objective optimization and outperforms a widely used and well known multi-objective evolutionary algorithm.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133392794","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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