Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516266
Rutul Patel, Pragnesh V. Patel, Jinendar Lalwani, M. Sarkar, S. Nagaraj
In the Wireless Body Area Network (WBAN), radio propagations from devices that are near or inside the human body are composite and unique contrasting with the different environments since the human body has a composite shape comprising of various tissues. Along these lines, channel models are unique in relation to the ones in alternate situations. We present a channel modelling of a situation imitating to the real one where multiple transmitters are placed in a human brain and a receiver is placed on the human skull. Results like Packet Collisions, Received Signal Level and Impulse Response were obtained through experiments that are presented in the results section.
{"title":"Investigating the feasibility of multiple UWB transmitters in brain computer interface (BCI) applications","authors":"Rutul Patel, Pragnesh V. Patel, Jinendar Lalwani, M. Sarkar, S. Nagaraj","doi":"10.1109/BSN.2016.7516266","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516266","url":null,"abstract":"In the Wireless Body Area Network (WBAN), radio propagations from devices that are near or inside the human body are composite and unique contrasting with the different environments since the human body has a composite shape comprising of various tissues. Along these lines, channel models are unique in relation to the ones in alternate situations. We present a channel modelling of a situation imitating to the real one where multiple transmitters are placed in a human brain and a receiver is placed on the human skull. Results like Packet Collisions, Received Signal Level and Impulse Response were obtained through experiments that are presented in the results section.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"40 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":"121189903","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.2016.7516277
K. Seng, Ying Chen, K. M. A. Chai, Ting Wang, David Chiok Yuen Fun, Y. S. Teo, P. Tan, W. Ang, J. Lee
Military personnel operating in hot and humid environments are susceptible to heat-related illnesses. As heat-related illnesses are associated with a rise in body core temperature (Tc), a reliable system for real-time assessment of Tc is useful to minimize heat casualties. However, invasive measurement of Tc (such as rectal, intestinal and esophageal temperature) is impractical in the field settings. This paper describes the model construction and qualification results of tracking Tc using an extended Kalman filter (EKF) based on physiological data recorded from wearable sensors. Tc, surface skin temperature (Tsk) and heart rate (HR) data were collected from three studies with different experimental protocols, climatic conditions and soldier volunteers. The predictive performance of the model was evaluated by cross-validation and external validation. The final EKF model was implemented using a nonlinear (cubic) state-space model (Tsk versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR) as the time update model. Overall, when tested against an independent dataset, the model showed a prediction bias of 0.11°C, a root mean square deviation of 0.29°C, and 87% of all Tc predictions fell within ±0.3°C of the measured Tc values. The results from our study indicate that the derived EKF model is accurate enough to calculate subject-specific Tc for the minimization of heat injuries.
在炎热潮湿的环境中工作的军事人员容易患与热有关的疾病。由于热相关疾病与身体核心温度(Tc)的升高有关，一个可靠的实时评估Tc的系统有助于减少热伤亡。然而，有创测量Tc(如直肠、肠道和食管温度)在现场是不切实际的。本文介绍了基于可穿戴传感器记录的生理数据，利用扩展卡尔曼滤波(EKF)对Tc进行跟踪的模型构建和验证结果。Tc、体表皮肤温度(Tsk)和心率(HR)数据收集自三个不同实验方案、气候条件和士兵志愿者的研究。通过交叉验证和外部验证对模型的预测性能进行评价。最终的EKF模型是使用非线性(三次)状态空间模型(Tsk vs . Tc)和一个分阶段、自回归的外生模型(包含HR)作为时间更新模型来实现的。总体而言，当针对独立数据集进行测试时，该模型的预测偏差为0.11°C，均方根偏差为0.29°C, 87%的Tc预测落在测量Tc值的±0.3°C范围内。我们的研究结果表明，导出的EKF模型足够精确，可以计算受试者特定的Tc，以最大限度地减少热损伤。
{"title":"Tracking body core temperature in military thermal environments: An extended Kalman filter approach","authors":"K. Seng, Ying Chen, K. M. A. Chai, Ting Wang, David Chiok Yuen Fun, Y. S. Teo, P. Tan, W. Ang, J. Lee","doi":"10.1109/BSN.2016.7516277","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516277","url":null,"abstract":"Military personnel operating in hot and humid environments are susceptible to heat-related illnesses. As heat-related illnesses are associated with a rise in body core temperature (Tc), a reliable system for real-time assessment of Tc is useful to minimize heat casualties. However, invasive measurement of Tc (such as rectal, intestinal and esophageal temperature) is impractical in the field settings. This paper describes the model construction and qualification results of tracking Tc using an extended Kalman filter (EKF) based on physiological data recorded from wearable sensors. Tc, surface skin temperature (Tsk) and heart rate (HR) data were collected from three studies with different experimental protocols, climatic conditions and soldier volunteers. The predictive performance of the model was evaluated by cross-validation and external validation. The final EKF model was implemented using a nonlinear (cubic) state-space model (Tsk versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR) as the time update model. Overall, when tested against an independent dataset, the model showed a prediction bias of 0.11°C, a root mean square deviation of 0.29°C, and 87% of all Tc predictions fell within ±0.3°C of the measured Tc values. The results from our study indicate that the derived EKF model is accurate enough to calculate subject-specific Tc for the minimization of heat injuries.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"75 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114005508","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.2016.7516261
Jacob L. Griffith, A. Wakim, P. Moore-Jansen, K. Cluff
Summary form only given. This study was focused on the development of a non-invasive mobile skin sensor for measuring intracranial pressure (ICP). Current techniques are limited to surgical implantations or methods that require highly specialized equipment and training. Additionally, surgical implantations carry risk of infection. To overcome these disadvantages we designed an electromagnetic resonance skin sensor patch to measure ICP. Fluctuations in the sensor's magnetic field were correlated with volumetric changes inside the cranial cavity of a human skull. These results provide evidence for an innovative method which avoids the disadvantages of current methods.
{"title":"Non-invasive biomedical patch sensor to measure intracranial pressure","authors":"Jacob L. Griffith, A. Wakim, P. Moore-Jansen, K. Cluff","doi":"10.1109/BSN.2016.7516261","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516261","url":null,"abstract":"Summary form only given. This study was focused on the development of a non-invasive mobile skin sensor for measuring intracranial pressure (ICP). Current techniques are limited to surgical implantations or methods that require highly specialized equipment and training. Additionally, surgical implantations carry risk of infection. To overcome these disadvantages we designed an electromagnetic resonance skin sensor patch to measure ICP. Fluctuations in the sensor's magnetic field were correlated with volumetric changes inside the cranial cavity of a human skull. These results provide evidence for an innovative method which avoids the disadvantages of current methods.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"16 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":"126308606","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.2016.7516276
R. Brugarolas, M. T. Agcayazi, S. Yuschak, D. Roberts, B. Sherman, A. Bozkurt
Although numerous advances have been made in instrumental odor detection systems, these still cannot match the efficient sampling, odor discrimination, agile mobility and the olfactory acuity of odor detection dogs. A limiting step in using dogs to detect odors is the subjectivity of the translation of odor information processed by the dog to its handler. We present our preliminary efforts towards a wireless wearable system for continuous auscultation of respiratory behavior by recording internal sounds at the neck and chest by means of a commercially available electronic stethoscope to provide objective decision support for handlers. We have identified discrete features of sniffing and panting in the time domain and utilize event duration, event rate, event mean energy, and the number of consecutive events in a row to build a decision tree classifier. Since feature extraction requires segmentation of individual sniffing and panting events, we developed an adaptive method using short-time energy contour and an adaptive threshold. The performance of the system was evaluated on recordings from a Greyhound and a Labrador Retriever and achieved high classification accuracies.
{"title":"Towards a wearable system for continuous monitoring of sniffing and panting in dogs","authors":"R. Brugarolas, M. T. Agcayazi, S. Yuschak, D. Roberts, B. Sherman, A. Bozkurt","doi":"10.1109/BSN.2016.7516276","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516276","url":null,"abstract":"Although numerous advances have been made in instrumental odor detection systems, these still cannot match the efficient sampling, odor discrimination, agile mobility and the olfactory acuity of odor detection dogs. A limiting step in using dogs to detect odors is the subjectivity of the translation of odor information processed by the dog to its handler. We present our preliminary efforts towards a wireless wearable system for continuous auscultation of respiratory behavior by recording internal sounds at the neck and chest by means of a commercially available electronic stethoscope to provide objective decision support for handlers. We have identified discrete features of sniffing and panting in the time domain and utilize event duration, event rate, event mean energy, and the number of consecutive events in a row to build a decision tree classifier. Since feature extraction requires segmentation of individual sniffing and panting events, we developed an adaptive method using short-time energy contour and an adaptive threshold. The performance of the system was evaluated on recordings from a Greyhound and a Labrador Retriever and achieved high classification accuracies.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"31 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":"130225729","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.2016.7516271
Sriram Raju Dandu, M. Engelhard, M. Goldman, J. Lach
Gait impairment in Multiple Sclerosis (MS) can result from imbalance, physical fatigue, weakness, and other symptoms. Walking speed is the primary measure of gait impairment used by clinical researchers, but inertial gait features from body-worn sensors have been proven to add clinical value. This paper seeks to understand the physiologic significance of two such features in MS. Both features are computed using the dynamic time warping (DTW) algorithm: the “DTW Score” is based on the usual DTW distance, and the “Warp Score” is based on the warping length. Using linear regression and stepwise regression models, the relationship between these features and several gait-related MS symptoms is analyzed. Results show that the DTW Score and Warp Score have distinct physiologic significance in MS compared to walking speed, and these features may also be useful for walking assessment in a wide range of clinical contexts.
{"title":"Determining physiological significance of inertial gait features in multiple sclerosis","authors":"Sriram Raju Dandu, M. Engelhard, M. Goldman, J. Lach","doi":"10.1109/BSN.2016.7516271","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516271","url":null,"abstract":"Gait impairment in Multiple Sclerosis (MS) can result from imbalance, physical fatigue, weakness, and other symptoms. Walking speed is the primary measure of gait impairment used by clinical researchers, but inertial gait features from body-worn sensors have been proven to add clinical value. This paper seeks to understand the physiologic significance of two such features in MS. Both features are computed using the dynamic time warping (DTW) algorithm: the “DTW Score” is based on the usual DTW distance, and the “Warp Score” is based on the warping length. Using linear regression and stepwise regression models, the relationship between these features and several gait-related MS symptoms is analyzed. Results show that the DTW Score and Warp Score have distinct physiologic significance in MS compared to walking speed, and these features may also be useful for walking assessment in a wide range of clinical contexts.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"71 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":"132939785","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.2016.7516260
Pragnesh V. Patel, J. A. Kumar, M. Sarkar, S. Nagaraj
Ultra wideband (UWB) radio technology has been shown to have tremendous potential to support certain Brain Computer Interface (BCI) applications. To implant UWB transmitters inside a human brain to transmit ECoG signals from the neurons in the brain (collected by bio-implantable electrodes) to stimulators residing on the spinal cord or on muscles at different parts of the body (non-invasive), almost sounds like a sci-fi story. In order to make this sci-fi dream a reality, many aspects of the problem needs to be addressed. In this paper, we attempt to study one such aspect. Specifically, we study the behavior of UWB transmissions through the human brain, primarily focusing on the behavior of the signal when it traverses through human blood in the brain before it can be received by the receiver planted on the surface of the human body (spinal cord, upper limbs, etc). We have performed in-depth numerical analysis through theoretical and experimental procedures which has helped us gain insight into the attenuation, delay and transmit power properties of the signal at different frequencies within the UWB spectrum. We present these results in this paper. So far, there are no channel models available for UWB transmissions inside the human body. Our work, is a step in that direction.
{"title":"Tracking the behavior of UWB transmissions in invasive BCI applications","authors":"Pragnesh V. Patel, J. A. Kumar, M. Sarkar, S. Nagaraj","doi":"10.1109/BSN.2016.7516260","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516260","url":null,"abstract":"Ultra wideband (UWB) radio technology has been shown to have tremendous potential to support certain Brain Computer Interface (BCI) applications. To implant UWB transmitters inside a human brain to transmit ECoG signals from the neurons in the brain (collected by bio-implantable electrodes) to stimulators residing on the spinal cord or on muscles at different parts of the body (non-invasive), almost sounds like a sci-fi story. In order to make this sci-fi dream a reality, many aspects of the problem needs to be addressed. In this paper, we attempt to study one such aspect. Specifically, we study the behavior of UWB transmissions through the human brain, primarily focusing on the behavior of the signal when it traverses through human blood in the brain before it can be received by the receiver planted on the surface of the human body (spinal cord, upper limbs, etc). We have performed in-depth numerical analysis through theoretical and experimental procedures which has helped us gain insight into the attenuation, delay and transmit power properties of the signal at different frequencies within the UWB spectrum. We present these results in this paper. So far, there are no channel models available for UWB transmissions inside the human body. Our work, is a step in that direction.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"218 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":"115634542","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.2016.7516244
M. C. Cuervo, J. C. Álvarez, D. Álvarez
A tool used to capture biomechanical signals from the upper limb is presented here. The methods used for data acquisition, signal fusion and joint amplitude measurements, elbow flexion/extension and pronation/supination, are also presented. To that aim, a device using two inertial and magnetic sensors fixed to the arm and the forearm body segments was implemented. These sensors are wired to a control unit, which process them and sends the information to a display device through a wireless communication protocol. This process can be done in real time, or the results can be stored and managed later. This device was applied to an industrial robot arm, and the results were compared to the actual rotation values. Experiments showed a RMSE of 2.19° in flexion/extension and of 2.75° in pronation/supination. As a conclusion, it can be claimed that the system has an acceptable level of precision to be used as a support tool in rehabilitation processes of people with slight motor damages.
{"title":"Capture and analysis of biomechanical signals with inertial and magnetic sensors as support in physical rehabilitation processes","authors":"M. C. Cuervo, J. C. Álvarez, D. Álvarez","doi":"10.1109/BSN.2016.7516244","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516244","url":null,"abstract":"A tool used to capture biomechanical signals from the upper limb is presented here. The methods used for data acquisition, signal fusion and joint amplitude measurements, elbow flexion/extension and pronation/supination, are also presented. To that aim, a device using two inertial and magnetic sensors fixed to the arm and the forearm body segments was implemented. These sensors are wired to a control unit, which process them and sends the information to a display device through a wireless communication protocol. This process can be done in real time, or the results can be stored and managed later. This device was applied to an industrial robot arm, and the results were compared to the actual rotation values. Experiments showed a RMSE of 2.19° in flexion/extension and of 2.75° in pronation/supination. As a conclusion, it can be claimed that the system has an acceptable level of precision to be used as a support tool in rehabilitation processes of people with slight motor damages.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"29 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":"114994821","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.2016.7516284
Federico Parisi, G. Ferrari, A. Baricich, M. D'Innocenzo, C. Cisari, A. Mauro
Improving independent mobility in post-stroke patients is one of the main goals of most rehabilitation strategies. While quantitative gait assessment is crucial to provide a meaningful feedback on the recovery progress, the irregularity of hemiparetic walking prevents the use of classical Inertial Measurement Unit (IMU)-based gait analysis algorithms. In this paper, we propose a novel low-cost system, which relies on a single wearable IMU attached to the lower trunk, to estimate spatio-temporal gait parameters of both hemiparetic and healthy subjects. A new procedure for temporal features' computation and two modified versions of well-known step length (i.e., spatial features) estimators are derived. In both cases, we exploit dynamic calibration constants, related to the “power” of an individual gait pattern, to deal with the typical asymmetry and inter-subject variability of hemiparetic gait. The spatio-temporal features estimated with the proposed methods are compared with ground-truth parameters extracted by an optoelectronic system. The obtained results show very high correlations between estimated and reference values.
{"title":"Accurate gait analysis in post-stroke patients using a single inertial measurement unit","authors":"Federico Parisi, G. Ferrari, A. Baricich, M. D'Innocenzo, C. Cisari, A. Mauro","doi":"10.1109/BSN.2016.7516284","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516284","url":null,"abstract":"Improving independent mobility in post-stroke patients is one of the main goals of most rehabilitation strategies. While quantitative gait assessment is crucial to provide a meaningful feedback on the recovery progress, the irregularity of hemiparetic walking prevents the use of classical Inertial Measurement Unit (IMU)-based gait analysis algorithms. In this paper, we propose a novel low-cost system, which relies on a single wearable IMU attached to the lower trunk, to estimate spatio-temporal gait parameters of both hemiparetic and healthy subjects. A new procedure for temporal features' computation and two modified versions of well-known step length (i.e., spatial features) estimators are derived. In both cases, we exploit dynamic calibration constants, related to the “power” of an individual gait pattern, to deal with the typical asymmetry and inter-subject variability of hemiparetic gait. The spatio-temporal features estimated with the proposed methods are compared with ground-truth parameters extracted by an optoelectronic system. The obtained results show very high correlations between estimated and reference values.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"44 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":"116969621","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.2016.7516229
B. Misgeld, Markus J. Lüken, S. Leonhardt
The accurate, real-time estimation of biomechanical joint parameters bears a potential benefit for many applications. Examples include the assessment of training success in movement therapy, the use as a quantitative clinical scale for joint rigidity or the use in the derivation of control parameters for active, intelligent orthotic or prosthetic devices. Such a realtime assessment system should be as unobtrusive as possible, minimising instrumentation effort for the user or the clinical staff. Towards this goal we have build a body sensor network (BSN) that is able to measure surface electromyogram and 9-degrees of freedom inertial/magnetic data at high sample rates. The measured data is preprocessed and subsequently used in an Unscented Kalman Filter in a model-based approach employing the nonlinear dynamics of the human knee kinematics. The derivation of biomechanical joint parameters, in our case the knee stiffness, can then be readily obtained from the nonlinear model. To validate BSN measurements, we present a novel test-bench and its corresponding nonlinear model. The biomechanical parameter estimator is validated in pendulum like motions on the test-bench and in experiments where the test subject is undergoing co-activation of extensor and flexor muscles acting on the knee.
{"title":"Identification of isolated biomechanical parameters with a wireless body sensor network","authors":"B. Misgeld, Markus J. Lüken, S. Leonhardt","doi":"10.1109/BSN.2016.7516229","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516229","url":null,"abstract":"The accurate, real-time estimation of biomechanical joint parameters bears a potential benefit for many applications. Examples include the assessment of training success in movement therapy, the use as a quantitative clinical scale for joint rigidity or the use in the derivation of control parameters for active, intelligent orthotic or prosthetic devices. Such a realtime assessment system should be as unobtrusive as possible, minimising instrumentation effort for the user or the clinical staff. Towards this goal we have build a body sensor network (BSN) that is able to measure surface electromyogram and 9-degrees of freedom inertial/magnetic data at high sample rates. The measured data is preprocessed and subsequently used in an Unscented Kalman Filter in a model-based approach employing the nonlinear dynamics of the human knee kinematics. The derivation of biomechanical joint parameters, in our case the knee stiffness, can then be readily obtained from the nonlinear model. To validate BSN measurements, we present a novel test-bench and its corresponding nonlinear model. The biomechanical parameter estimator is validated in pendulum like motions on the test-bench and in experiments where the test subject is undergoing co-activation of extensor and flexor muscles acting on the knee.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"68 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":"128653487","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.2016.7516243
Nikhil Shinde, K. George
The Brain-computer interface (BCI) is an engaging field which could find applications in numerous fields like industrial, biomedical and engineering. In this paper a BCI based electric wheelchair driving aid design that utilizes mental concentration (EEG signals) and eye blinks (EMG signals) of the user, is presented. The design incorporates a safety controller with peripheral safety sensors that override the user command and stop the wheelchair when it detects an obstacle in its path. The wheelchair driving aid design is cost-effective (estimated cost less than $200) as it utilizes off-the-shelf BCI headset and electronics. Four experiments were conducted to validate the performance and reliability of the design.
{"title":"Brain-controlled driving aid for electric wheelchairs","authors":"Nikhil Shinde, K. George","doi":"10.1109/BSN.2016.7516243","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516243","url":null,"abstract":"The Brain-computer interface (BCI) is an engaging field which could find applications in numerous fields like industrial, biomedical and engineering. In this paper a BCI based electric wheelchair driving aid design that utilizes mental concentration (EEG signals) and eye blinks (EMG signals) of the user, is presented. The design incorporates a safety controller with peripheral safety sensors that override the user command and stop the wheelchair when it detects an obstacle in its path. The wheelchair driving aid design is cost-effective (estimated cost less than $200) as it utilizes off-the-shelf BCI headset and electronics. Four experiments were conducted to validate the performance and reliability of the design.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"10 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":"129258733","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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