Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516281
B. Rosa, Guang-Zhong Yang
Ultrasound imaging is a proven diagnostic tool to assess a myriad of physiological and pathological conditions in patients. Throughout the years, ultrasounds have been used as a passive recording modality where the backscattered echo arising from the interaction of the sound waves with the acoustic properties of the biological tissues helps to identify them. Apart from a wide range of therapeutic applications, the acoustic beam has not yet been explored to actuate within the biological environment in an active way. In this paper we present an implantable electronic device to be actuated remotely by ultrasounds with capabilities for measuring several physiological parameters of tissues: pH, temperature, electrolyte concentration and biopotentials. The small factory form device (with no attached batteries) harvests energy from the incoming ultrasound waves and uses it to power the embedded electronics. It operates from voltage levels as low as 0.8 V and consuming a total current of 60 μA (or an average power consumption of 84 μW) in the active mode when deployed at a distance of 3 cm from the active source of ultrasounds in vitro, excited by a sinusoid at 400 kHz with power density of 20 mWcm-2. The sensor can be actuated by a specifically-designed readout device (as detailed in this paper) or using the traditional medical probes for ultrasound imaging. The actual device can present an alternative to surpass the limitations of inductive and RF-powered sensors implanted in soft tissues.
{"title":"Active implantable sensor powered by ultrasounds with application in the monitoring of physiological parameters for soft tissues","authors":"B. Rosa, Guang-Zhong Yang","doi":"10.1109/BSN.2016.7516281","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516281","url":null,"abstract":"Ultrasound imaging is a proven diagnostic tool to assess a myriad of physiological and pathological conditions in patients. Throughout the years, ultrasounds have been used as a passive recording modality where the backscattered echo arising from the interaction of the sound waves with the acoustic properties of the biological tissues helps to identify them. Apart from a wide range of therapeutic applications, the acoustic beam has not yet been explored to actuate within the biological environment in an active way. In this paper we present an implantable electronic device to be actuated remotely by ultrasounds with capabilities for measuring several physiological parameters of tissues: pH, temperature, electrolyte concentration and biopotentials. The small factory form device (with no attached batteries) harvests energy from the incoming ultrasound waves and uses it to power the embedded electronics. It operates from voltage levels as low as 0.8 V and consuming a total current of 60 μA (or an average power consumption of 84 μW) in the active mode when deployed at a distance of 3 cm from the active source of ultrasounds in vitro, excited by a sinusoid at 400 kHz with power density of 20 mWcm-2. The sensor can be actuated by a specifically-designed readout device (as detailed in this paper) or using the traditional medical probes for ultrasound imaging. The actual device can present an alternative to surpass the limitations of inductive and RF-powered sensors implanted in soft tissues.","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":"125445517","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.2016.7516289
Xiaoxu Wu, Xiaoyu Xu, Yan Wang, W. Kaiser, G. Pottie
Human activity monitoring systems using inertial sensors have found wide applications in the field of health and wellness by providing valuable information for diagnostics and rehabilitation processes to doctors and clinicians. As the scales of studies increase, sensor orientation placement errors have become one of the most commonly seen difficulties for such systems. Assuming patients to wear sensors at the correct orientation is unrealistic and will result in a large amount of data loss or distortion. In order to tackle this problem, we propose a double layer classification model. The first layer, not assuming correct sensor orientation, uses orientation-invariant accelerometer magnitude to construct a highly conservative walking detection model. The detected walking beacons from this layer are used to compare to the training template to obtain the true sensor orientation. Then proper rotation matrix can be applied to the whole day data, and fed into the second layer of a finer classifier where orientation-variant features are used. In order to show validity of this method, we hired 7 healthy subjects and 2 stroke patients in the rehab process to wear the sensors for two days and at least 6 hours each day. Ground truth are labeled manually with a Matlab GUI tool. Precision and recall for walking detection in each day are reported and discussed.
{"title":"A double-layer automatic orientation correction method for human activity recognition","authors":"Xiaoxu Wu, Xiaoyu Xu, Yan Wang, W. Kaiser, G. Pottie","doi":"10.1109/BSN.2016.7516289","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516289","url":null,"abstract":"Human activity monitoring systems using inertial sensors have found wide applications in the field of health and wellness by providing valuable information for diagnostics and rehabilitation processes to doctors and clinicians. As the scales of studies increase, sensor orientation placement errors have become one of the most commonly seen difficulties for such systems. Assuming patients to wear sensors at the correct orientation is unrealistic and will result in a large amount of data loss or distortion. In order to tackle this problem, we propose a double layer classification model. The first layer, not assuming correct sensor orientation, uses orientation-invariant accelerometer magnitude to construct a highly conservative walking detection model. The detected walking beacons from this layer are used to compare to the training template to obtain the true sensor orientation. Then proper rotation matrix can be applied to the whole day data, and fed into the second layer of a finer classifier where orientation-variant features are used. In order to show validity of this method, we hired 7 healthy subjects and 2 stroke patients in the rehab process to wear the sensors for two days and at least 6 hours each day. Ground truth are labeled manually with a Matlab GUI tool. Precision and recall for walking detection in each day are reported and discussed.","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":"129737763","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.2016.7516257
R. Richer, B. Groh, Peter Blank, Eva Dorschky, C. Martindale, J. Klucken, B. Eskofier
The possibilities for wearable health care technology to improve the quality of life for chronic disease patients has been increasing within recent years. For instance, unobtrusive cardiac monitoring can be applied to people suffering from a disorder of the autonomic nervous system (ANS) which show a significantly lower heart rate variability (HRV) than healthy people. Although recent work presented solutions to analyze this relationship, they did not perform it during daily life situations. For that reason, this work presents a system for a real-time analysis of the user's HRV on an Android-based mobile device throughout the day. The system was used for the detection of an orthostatic dysregulation which can be an indicator for a disorder of the ANS. Measures for HRV analysis were computed from acquired ECG data and compared before and after a posture change. For triggering the HRV analysis, an IMU-based algorithm which detects stand up events was developed. As a proof of concept for an automatic assessment of an orthostatic dysregulation, a classification based on the derived HRV measures was performed. The performance of the stand up detection was evaluated in the first part of this study. The second part was conducted for the evaluation of the derived HRV measures and involved healthy subjects as well as patients with idiopathic Parkinson's Disease. The results of the evaluation showed a recognition rate of 90.0 % for the stand up detection algorithm. Furthermore, a clear difference in the change of HRV measures between the two groups before and after standing up was observed. The classification provided an accuracy of 96.0%, and a sensitivity of 93.3%. The results demonstrated the possibility of unobtrusive HRV monitoring during daily life situations.
{"title":"Unobtrusive real-time heart rate variability analysis for the detection of orthostatic dysregulation","authors":"R. Richer, B. Groh, Peter Blank, Eva Dorschky, C. Martindale, J. Klucken, B. Eskofier","doi":"10.1109/BSN.2016.7516257","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516257","url":null,"abstract":"The possibilities for wearable health care technology to improve the quality of life for chronic disease patients has been increasing within recent years. For instance, unobtrusive cardiac monitoring can be applied to people suffering from a disorder of the autonomic nervous system (ANS) which show a significantly lower heart rate variability (HRV) than healthy people. Although recent work presented solutions to analyze this relationship, they did not perform it during daily life situations. For that reason, this work presents a system for a real-time analysis of the user's HRV on an Android-based mobile device throughout the day. The system was used for the detection of an orthostatic dysregulation which can be an indicator for a disorder of the ANS. Measures for HRV analysis were computed from acquired ECG data and compared before and after a posture change. For triggering the HRV analysis, an IMU-based algorithm which detects stand up events was developed. As a proof of concept for an automatic assessment of an orthostatic dysregulation, a classification based on the derived HRV measures was performed. The performance of the stand up detection was evaluated in the first part of this study. The second part was conducted for the evaluation of the derived HRV measures and involved healthy subjects as well as patients with idiopathic Parkinson's Disease. The results of the evaluation showed a recognition rate of 90.0 % for the stand up detection algorithm. Furthermore, a clear difference in the change of HRV measures between the two groups before and after standing up was observed. The classification provided an accuracy of 96.0%, and a sensitivity of 93.3%. The results demonstrated the possibility of unobtrusive HRV monitoring during daily life situations.","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":"130781975","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.2016.7516279
Lindsay W. Ludlow, P. Weyand
We used external loading and surface inclination as experimental tools to: 1) test whether the metabolic cost of transporting body mass and torso mass are equal during walking, and 2) to develop an algorithm for estimating walking metabolism in the field. Rates of oxygen uptake were measured in ten physically active volunteers during constant-velocity treadmill trials (0.6-1.4 m·s-1) on four grades (0-9°) under three loading conditions (1.0-1.31 times body mass). Walking metabolic rates (Egross-Erest) increased systematically with speed, load, and grade to span values from 2.2 to 14.2 times measured resting metabolic rates. When walking metabolism was expressed in relation to the total mass carried, loaded and unloaded metabolic rates were nearly identical across all conditions. The equivalent costs of transporting one kg of body and external mass allowed formulation of a promising estimation algorithm requiring only total mass, speed, and grade as inputs (R2=0.98; SEE=0.37 W·kg-1; n=360 trials).
{"title":"Estimating loaded, inclined walking energetics: No functional difference between added and body mass","authors":"Lindsay W. Ludlow, P. Weyand","doi":"10.1109/BSN.2016.7516279","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516279","url":null,"abstract":"We used external loading and surface inclination as experimental tools to: 1) test whether the metabolic cost of transporting body mass and torso mass are equal during walking, and 2) to develop an algorithm for estimating walking metabolism in the field. Rates of oxygen uptake were measured in ten physically active volunteers during constant-velocity treadmill trials (0.6-1.4 m·s-1) on four grades (0-9°) under three loading conditions (1.0-1.31 times body mass). Walking metabolic rates (Egross-Erest) increased systematically with speed, load, and grade to span values from 2.2 to 14.2 times measured resting metabolic rates. When walking metabolism was expressed in relation to the total mass carried, loaded and unloaded metabolic rates were nearly identical across all conditions. The equivalent costs of transporting one kg of body and external mass allowed formulation of a promising estimation algorithm requiring only total mass, speed, and grade as inputs (R2=0.98; SEE=0.37 W·kg-1; n=360 trials).","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"23 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":"125073192","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.2016.7516230
Lars Büthe, Ulf Blanke, Haralds Capkevics, G. Tröster
Wearables find in sports one of their main applications. In recent years, many wearable devices have been commercially released such as the Babolat Play or Sony Smart Tennis Sensor that detect and classify different types of tennis shots and provide a performance analysis to the player. However, available devices focus on a single technical element of tennis only - the shot. As tennis performance is the result of a full body coordination and timing of the movement, the present work wants to take a broader view at the tennis player performance and include the simultaneous work of legs and arms with the goal to time elements of movement. We design a sensor system with three inertial measurement units, one attached to each foot as well as one at the racket. We develop a pipeline to detect and classify leg and arm movement and implement a gesture recognition for the shooting arm based on LCSS (longest common subsequence). The algorithm distinguishes between forehand and backhand (with topspin and slice, respectively) as well as a smash. Footwork is first segmented into potential steps and then classified by a support vector machine between shot and side steps. In the person-dependent case the algorithm achieved 87% recall and 89% precision. The step recognition algorithm has been able to detect 76% of the steps with a classification accuracy of 95%. Based on these results timing information within the shooting state can be robustly obtained which is crucial for a thorough analysis of the whole shot.
{"title":"A wearable sensing system for timing analysis in tennis","authors":"Lars Büthe, Ulf Blanke, Haralds Capkevics, G. Tröster","doi":"10.1109/BSN.2016.7516230","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516230","url":null,"abstract":"Wearables find in sports one of their main applications. In recent years, many wearable devices have been commercially released such as the Babolat Play or Sony Smart Tennis Sensor that detect and classify different types of tennis shots and provide a performance analysis to the player. However, available devices focus on a single technical element of tennis only - the shot. As tennis performance is the result of a full body coordination and timing of the movement, the present work wants to take a broader view at the tennis player performance and include the simultaneous work of legs and arms with the goal to time elements of movement. We design a sensor system with three inertial measurement units, one attached to each foot as well as one at the racket. We develop a pipeline to detect and classify leg and arm movement and implement a gesture recognition for the shooting arm based on LCSS (longest common subsequence). The algorithm distinguishes between forehand and backhand (with topspin and slice, respectively) as well as a smash. Footwork is first segmented into potential steps and then classified by a support vector machine between shot and side steps. In the person-dependent case the algorithm achieved 87% recall and 89% precision. The step recognition algorithm has been able to detect 76% of the steps with a classification accuracy of 95%. Based on these results timing information within the shooting state can be robustly obtained which is crucial for a thorough analysis of the whole shot.","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":"126093742","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.2016.7516227
Xueliang Bao, Z. Bi, Haipeng Wang, Yuxuan Zhou, Xiaoying Lü, Zhigong Wang
In this study, we present an alternating controlled functional electrical stimulation (FES) strategy for rehabilitation of lower extremity motor function of hemiplegia after stroke. The muscle activity onset time, determined by using sample entropy (SampEn) analysis of an electromyographic (EMG) signal, is used as a trigger for FES to manage stimulations. The EMG-bridge (EMGB) type FES is a novel motor functional rehabilitation idea that it exploits sEMG signal from a healthy limb to regulate the stimulus parameters of stimulations applied to the paralyzed limb, so as to achieve synchronous movement of bilateral or different limbs. The alternating controlled FES strategy was realized on the basis of combing muscle activity onset time with EMGB-type FES system. Using this FES control strategy, experiments on a healthy subject have been carried out successfully to realize alternating stimulation to plantar flexor (PF) and dorsiflexor (DF) muscles of lower limb in sitting position.
{"title":"An alternating controlled functional electrical stimulation strategy based on sample entropy for rehabilitation of lower extremity hemiplegia","authors":"Xueliang Bao, Z. Bi, Haipeng Wang, Yuxuan Zhou, Xiaoying Lü, Zhigong Wang","doi":"10.1109/BSN.2016.7516227","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516227","url":null,"abstract":"In this study, we present an alternating controlled functional electrical stimulation (FES) strategy for rehabilitation of lower extremity motor function of hemiplegia after stroke. The muscle activity onset time, determined by using sample entropy (SampEn) analysis of an electromyographic (EMG) signal, is used as a trigger for FES to manage stimulations. The EMG-bridge (EMGB) type FES is a novel motor functional rehabilitation idea that it exploits sEMG signal from a healthy limb to regulate the stimulus parameters of stimulations applied to the paralyzed limb, so as to achieve synchronous movement of bilateral or different limbs. The alternating controlled FES strategy was realized on the basis of combing muscle activity onset time with EMGB-type FES system. Using this FES control strategy, experiments on a healthy subject have been carried out successfully to realize alternating stimulation to plantar flexor (PF) and dorsiflexor (DF) muscles of lower limb in sitting position.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"123 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":"115040809","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.2016.7516228
Joachim F. Kreutzer, Jannai Flaschberger, C. M. Hein, T. Lüth
This contribution presents a smart cup that uses a capacitive sensor to detect its filling level in order to monitor fluid intake over time. Dehydration is frequently diagnosed in hospitals among the elderly and connected to numerous sequelae and deaths. An automated monitoring system that detects daily fluid intake of a patient could reduce the vulnerability to dehydration and therefore the vast expenses that are associated with this condition. The smart cup obtains the current filling level from a capacitive sensor consisting of multiple serially arranged discrete electrodes. It is placed on the outside surface of its wall and shielded against external disturbances. Sensor data is processed applying multiple signal filters and error correction methods. Drinking volume of beverages at room temperature is detected accurately and reliably but error rate rises for very cold or hot liquids. The prototype integrates all components in a compact way, is dishwasher-safe and can be charged inductively. Data is transmitted to a base station via Bluetooth Low Energy. This way, a monitoring device is presented which will help preventing dehydration of elderly people.
{"title":"Capacitive detection of filling levels in a cup","authors":"Joachim F. Kreutzer, Jannai Flaschberger, C. M. Hein, T. Lüth","doi":"10.1109/BSN.2016.7516228","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516228","url":null,"abstract":"This contribution presents a smart cup that uses a capacitive sensor to detect its filling level in order to monitor fluid intake over time. Dehydration is frequently diagnosed in hospitals among the elderly and connected to numerous sequelae and deaths. An automated monitoring system that detects daily fluid intake of a patient could reduce the vulnerability to dehydration and therefore the vast expenses that are associated with this condition. The smart cup obtains the current filling level from a capacitive sensor consisting of multiple serially arranged discrete electrodes. It is placed on the outside surface of its wall and shielded against external disturbances. Sensor data is processed applying multiple signal filters and error correction methods. Drinking volume of beverages at room temperature is detected accurately and reliably but error rate rises for very cold or hot liquids. The prototype integrates all components in a compact way, is dishwasher-safe and can be charged inductively. Data is transmitted to a base station via Bluetooth Low Energy. This way, a monitoring device is presented which will help preventing dehydration of elderly people.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"36 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":"115111387","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.2016.7516251
Alex Resendiz, Dani Odicho, V. Gabrielian, A. Nahapetian
Fluid retention, known medically as edema, is caused by the retention of fluid in the soft tissue of the lower extremities. This is most commonly found in the ankles and feet due to the effects of gravity. In this paper, we present a wearable device worn around the ankle that monitors edema in the legs and alerts the user of changes. We discuss the Edemeter system's physical and functional design. We also present results from several experiments characterizing the use of flex sensors for measuring ankle swelling, as well as system component power consumption and its impact on battery life.
{"title":"Edemeter: Wearable and continuous fluid retention monitoring","authors":"Alex Resendiz, Dani Odicho, V. Gabrielian, A. Nahapetian","doi":"10.1109/BSN.2016.7516251","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516251","url":null,"abstract":"Fluid retention, known medically as edema, is caused by the retention of fluid in the soft tissue of the lower extremities. This is most commonly found in the ankles and feet due to the effects of gravity. In this paper, we present a wearable device worn around the ankle that monitors edema in the legs and alerts the user of changes. We discuss the Edemeter system's physical and functional design. We also present results from several experiments characterizing the use of flex sensors for measuring ankle swelling, as well as system component power consumption and its impact on battery life.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"5 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113946428","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 : 2016-06-01DOI: 10.1109/BSN.2016.7516280
Denis Huen, Jindong Liu, Benny P. L. Lo
Tremor is a neurological disorder which can significantly impede the daily functions of patients. The available treatments for patients with tremor are mainly pharmacotherapy and neurosurgery, but these treatments often have side effects. A wearable exoskeleton can potentially provide the assistance needed for patients with Parkinsonian or essential tremor to carry out daily activities and enable independent living. This paper presents the design and development of a 3D printed lightweight tremor suppression wearable exoskeleton. One of the major technical challenges for wearable robot is to maintain long battery life meanwhile miniature in size for practical use. This paper proposes an integrated approach where context aware Body Sensor Networks (BSN) sensors are incorporated to characterize voluntary and tremor movement, and detect activities of daily life (ADL). With the contextual information, the system can determine the intention of the user, optimize its control and minimize its power consumption by providing the necessary suppression only when needed. The preliminary result has shown that the wearable robot prototype can reduce the amplitude of simulated tremor by around 77%, and accurately identify different ADL with accuracy above 70%.
{"title":"An integrated wearable robot for tremor suppression with context aware sensing","authors":"Denis Huen, Jindong Liu, Benny P. L. Lo","doi":"10.1109/BSN.2016.7516280","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516280","url":null,"abstract":"Tremor is a neurological disorder which can significantly impede the daily functions of patients. The available treatments for patients with tremor are mainly pharmacotherapy and neurosurgery, but these treatments often have side effects. A wearable exoskeleton can potentially provide the assistance needed for patients with Parkinsonian or essential tremor to carry out daily activities and enable independent living. This paper presents the design and development of a 3D printed lightweight tremor suppression wearable exoskeleton. One of the major technical challenges for wearable robot is to maintain long battery life meanwhile miniature in size for practical use. This paper proposes an integrated approach where context aware Body Sensor Networks (BSN) sensors are incorporated to characterize voluntary and tremor movement, and detect activities of daily life (ADL). With the contextual information, the system can determine the intention of the user, optimize its control and minimize its power consumption by providing the necessary suppression only when needed. The preliminary result has shown that the wearable robot prototype can reduce the amplitude of simulated tremor by around 77%, and accurately identify different ADL with accuracy above 70%.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126870968","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 : 2016-04-22DOI: 10.1109/BSN.2016.7516274
J. Williamson, A. Hess, Christopher J. Smalt, D. Sherrill, T. Quatieri, C. O'Brien
Military working dogs (MWDs) are at high risk of heat strain both during training and missions. Body heat in a MWD increases due to work, and the primary means for reducing this heat are resting and panting. Body-worn sensors can enable monitoring of work level and respiratory rate in real time. They can thereby provide real-time objective indicators of thermal strain in MWDs. In this paper a system is proposed for using collar-worn accelerometer, global positioning system (GPS), and audio recorder sensors to provide real-time estimates of work level and respiration (breathing and panting) rate. Automated methods are demonstrated for using a collar-worn accelerometer and GPS sensor to estimate work levels during multiple short-duration activities, and for estimating respiration rates from a collar-worn audio recorder. The potential utility of these estimates for forecasting and monitoring thermal strain is assessed based on performance in out of sample prediction of core temperature (Tc) statistics, which are obtained from ingestible sensors. Using cross-validation, regression models are trained from accelerometer- and GPS-based activity estimates to predict rate of change in Tc, obtaining a correlation of r=0.59 between actual and predicted Tc change rates. Regression models are also trained from audio-based respiration rate estimates during recovery to predict the Tc values immediately prior to recovery, obtaining a correlation of r=0.49 between actual and predicted Tc.
{"title":"Using collar-worn sensors to forecast thermal strain in military working dogs","authors":"J. Williamson, A. Hess, Christopher J. Smalt, D. Sherrill, T. Quatieri, C. O'Brien","doi":"10.1109/BSN.2016.7516274","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516274","url":null,"abstract":"Military working dogs (MWDs) are at high risk of heat strain both during training and missions. Body heat in a MWD increases due to work, and the primary means for reducing this heat are resting and panting. Body-worn sensors can enable monitoring of work level and respiratory rate in real time. They can thereby provide real-time objective indicators of thermal strain in MWDs. In this paper a system is proposed for using collar-worn accelerometer, global positioning system (GPS), and audio recorder sensors to provide real-time estimates of work level and respiration (breathing and panting) rate. Automated methods are demonstrated for using a collar-worn accelerometer and GPS sensor to estimate work levels during multiple short-duration activities, and for estimating respiration rates from a collar-worn audio recorder. The potential utility of these estimates for forecasting and monitoring thermal strain is assessed based on performance in out of sample prediction of core temperature (Tc) statistics, which are obtained from ingestible sensors. Using cross-validation, regression models are trained from accelerometer- and GPS-based activity estimates to predict rate of change in Tc, obtaining a correlation of r=0.59 between actual and predicted Tc change rates. Regression models are also trained from audio-based respiration rate estimates during recovery to predict the Tc values immediately prior to recovery, obtaining a correlation of r=0.49 between actual and predicted Tc.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132215935","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：