Falls are dangerous for the aged population as they result in serious detrimental consequences. Therefore, many fall detection methods have been proposed. Most of these methods characterize falls by large accelerations and fast body orientation changes. However, certain activities like sitting down quickly, vigorous gaits, and jumping, also show these characteristics, and thus are hard to distinguish from real falls. Moreover, many falls in the elderly are slow falls which show lower activity levels. Existing work fails to detect slow falls effectively because they only identify falls with high activity levels. In this paper, we present a grammar-based fall detection framework which not only better distinguishes fall-like activities from real falls, but also emphasizes the detection of slow falls. We utilize posture information extracted from on-body sensors and context information collected from sensors deployed in the house to reduce false positives. A fall in our framework is detected as a sequence of sensor events. We provide a context-free grammar to define these sequences so that the framework can be easily extended to detect more kinds of falls. Our case study shows that our method can distinguish various fall-like activities from real falls and can also effectively detect both fast falls and slow falls. The integration evaluation shows that our method achieves both high sensitivity and high specificity.
{"title":"Grammar-based, posture- and context-cognitive detection for falls with different activity levels","authors":"Qiang Li, J. Stankovic","doi":"10.1145/2077546.2077553","DOIUrl":"https://doi.org/10.1145/2077546.2077553","url":null,"abstract":"Falls are dangerous for the aged population as they result in serious detrimental consequences. Therefore, many fall detection methods have been proposed. Most of these methods characterize falls by large accelerations and fast body orientation changes. However, certain activities like sitting down quickly, vigorous gaits, and jumping, also show these characteristics, and thus are hard to distinguish from real falls. Moreover, many falls in the elderly are slow falls which show lower activity levels. Existing work fails to detect slow falls effectively because they only identify falls with high activity levels.\u0000 In this paper, we present a grammar-based fall detection framework which not only better distinguishes fall-like activities from real falls, but also emphasizes the detection of slow falls. We utilize posture information extracted from on-body sensors and context information collected from sensors deployed in the house to reduce false positives. A fall in our framework is detected as a sequence of sensor events. We provide a context-free grammar to define these sequences so that the framework can be easily extended to detect more kinds of falls. Our case study shows that our method can distinguish various fall-like activities from real falls and can also effectively detect both fast falls and slow falls. The integration evaluation shows that our method achieves both high sensitivity and high specificity.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"64 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86504532","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}
Activity monitoring using Body Sensor Networks(BSN) has gained much attention from the scientific community due to its recreational and medical applications. Suggested techniques for activity monitoring face two major problem. First, systems have to be trained for the individual subjects due to the heterogeneity of the BSN data. While most solutions can address this problem on a small data set, they have no mechanics for automatic scaling of the solution as the data set increases. Second, the battery limitations of the BSN severely limit the maximum deployment time for the continuous monitoring. This problem is often solved by shifting some processing to the local sensor nodes to avoid a very heavy communication cost. However, little work has been done to optimize the sensing and processing cost of the action recognition. In this paper, we propose an action recognition approach based on the BSN repository. We show how the information of a large repository can be automatically used to customize the processing on sensor nodes based on a limited and automated training process. We also investigate the power cost of such a repository mining approach on the sensor nodes based on our implementation. To assess the power requirement, we define an energy model for data sensing and processing. We demonstrate the relationship between the activity recognition precision and the power consumption of the system during continuous action monitoring. We demonstrate the energy effectiveness of our approach with a classification accuracy constraint based on limited data repository.
{"title":"Lightweight power aware and scalable movement monitoring for wearable computers: a mining and recognition technique at the fingertip of sensors","authors":"Vitali Loseu, Jerry Mannil, R. Jafari","doi":"10.1145/2077546.2077554","DOIUrl":"https://doi.org/10.1145/2077546.2077554","url":null,"abstract":"Activity monitoring using Body Sensor Networks(BSN) has gained much attention from the scientific community due to its recreational and medical applications. Suggested techniques for activity monitoring face two major problem. First, systems have to be trained for the individual subjects due to the heterogeneity of the BSN data. While most solutions can address this problem on a small data set, they have no mechanics for automatic scaling of the solution as the data set increases. Second, the battery limitations of the BSN severely limit the maximum deployment time for the continuous monitoring. This problem is often solved by shifting some processing to the local sensor nodes to avoid a very heavy communication cost. However, little work has been done to optimize the sensing and processing cost of the action recognition. In this paper, we propose an action recognition approach based on the BSN repository. We show how the information of a large repository can be automatically used to customize the processing on sensor nodes based on a limited and automated training process. We also investigate the power cost of such a repository mining approach on the sensor nodes based on our implementation. To assess the power requirement, we define an energy model for data sensing and processing. We demonstrate the relationship between the activity recognition precision and the power consumption of the system during continuous action monitoring. We demonstrate the energy effectiveness of our approach with a classification accuracy constraint based on limited data repository.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"91 1","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89962450","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}
M. Altini, Salvatore Polito, J. Penders, Hyejung Kim, N. V. Helleputte, Sunyoung Kim, R. Yazicioglu
This paper presents the development of an ECG patch aiming at long term patient monitoring. The use of the recently standardized Bluetooth Low Energy (BLE) technology, together with a customized ultra-low-power ECG System on Chip (ECG SoC). including Digital Signal Processing (DSP) capabilities, enables the design of ultra low power systems able to continuously monitor patients, performing on board signal processing such as filtering, data compression, beat detection and motion artifact removal along with all the advantages provided by a standard radio technology such as Bluetooth. Early tests show how combining the ECG SoC and BLE leads to a total current consumption of only 500μA at 3.7V, while computing beat detection and transmitting heart rate remotely via BLE. This allows up to one month lifetime with a 400mAh Li-Po battery.
{"title":"An ECG patch combining a customized ultra-low-power ECG SoC with Bluetooth low energy for long term ambulatory monitoring","authors":"M. Altini, Salvatore Polito, J. Penders, Hyejung Kim, N. V. Helleputte, Sunyoung Kim, R. Yazicioglu","doi":"10.1145/2077546.2077564","DOIUrl":"https://doi.org/10.1145/2077546.2077564","url":null,"abstract":"This paper presents the development of an ECG patch aiming at long term patient monitoring. The use of the recently standardized Bluetooth Low Energy (BLE) technology, together with a customized ultra-low-power ECG System on Chip (ECG SoC). including Digital Signal Processing (DSP) capabilities, enables the design of ultra low power systems able to continuously monitor patients, performing on board signal processing such as filtering, data compression, beat detection and motion artifact removal along with all the advantages provided by a standard radio technology such as Bluetooth. Early tests show how combining the ECG SoC and BLE leads to a total current consumption of only 500μA at 3.7V, while computing beat detection and transmitting heart rate remotely via BLE. This allows up to one month lifetime with a 400mAh Li-Po battery.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"62 1","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79584890","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}
Assistive monitoring systems increasingly include cameras along with sensors. End-users require the capability to program such systems to monitor user-specified events and provide customized notifications in response. We introduce feature extractors, which provide a means for integrating camera video with sensor data. A feature extractor takes a video stream as input, and outputs a stream of integer values corresponding to the amount of a particular sensor phenomenon such as motion, sound, or light, or of more advanced phenomena such as human motion, screams, or falls. Feature extractors provide an elegant means for end-users to integrate cameras into their monitoring programs. We introduce feature extractors, provide examples illustrating their effectiveness for various common assistive monitoring scenarios, and summarize usability trials with 51 lay users demonstrating 56%-96% correct utilization of feature extractors.
{"title":"Feature extractors for integration of cameras and sensors during end-user programming of assistive monitoring systems","authors":"Alex D. Edgcomb, F. Vahid","doi":"10.1145/2077546.2077561","DOIUrl":"https://doi.org/10.1145/2077546.2077561","url":null,"abstract":"Assistive monitoring systems increasingly include cameras along with sensors. End-users require the capability to program such systems to monitor user-specified events and provide customized notifications in response. We introduce feature extractors, which provide a means for integrating camera video with sensor data. A feature extractor takes a video stream as input, and outputs a stream of integer values corresponding to the amount of a particular sensor phenomenon such as motion, sound, or light, or of more advanced phenomena such as human motion, screams, or falls. Feature extractors provide an elegant means for end-users to integrate cameras into their monitoring programs. We introduce feature extractors, provide examples illustrating their effectiveness for various common assistive monitoring scenarios, and summarize usability trials with 51 lay users demonstrating 56%-96% correct utilization of feature extractors.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"37 1","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82415723","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}
Bor-rong Chen, Shyamal Patel, Luca Della Toffola, P. Bonato
Activity recognition can provide important contextual information for the diagnosis and treatment of several medical conditions. In COPD patients, measurement of long term physical activity level, combined with physiological parameters such as heart rate and respiration rate can be used for early detection of exacerbations. Using wearable sensors, we can achieve this goal by continuously monitoring the daily activities of COPD patients. Due to low computation power of wearable sensors, typical activity monitoring systems are designed to store or wirelessly transfer raw data from the sensors to a more powerful PC-class computer for classification. While this approach preserves the original data at the highest resolution, it is highly resource-intensive and therefore reduces the lifetime of the wearable sensors due to required storage space, bandwidth, and battery capacity. In this demo, we present an optimized activity monitoring system for COPD patients that performs feature extraction on wearable sensors. Such implementation minimizes the number of radio packets sent by the wearable sensors and eliminates the need to store raw sensor data.
{"title":"Long-term monitoring of COPD using wearable sensors","authors":"Bor-rong Chen, Shyamal Patel, Luca Della Toffola, P. Bonato","doi":"10.1145/2077546.2077568","DOIUrl":"https://doi.org/10.1145/2077546.2077568","url":null,"abstract":"Activity recognition can provide important contextual information for the diagnosis and treatment of several medical conditions. In COPD patients, measurement of long term physical activity level, combined with physiological parameters such as heart rate and respiration rate can be used for early detection of exacerbations. Using wearable sensors, we can achieve this goal by continuously monitoring the daily activities of COPD patients. Due to low computation power of wearable sensors, typical activity monitoring systems are designed to store or wirelessly transfer raw data from the sensors to a more powerful PC-class computer for classification. While this approach preserves the original data at the highest resolution, it is highly resource-intensive and therefore reduces the lifetime of the wearable sensors due to required storage space, bandwidth, and battery capacity. In this demo, we present an optimized activity monitoring system for COPD patients that performs feature extraction on wearable sensors. Such implementation minimizes the number of radio packets sent by the wearable sensors and eliminates the need to store raw sensor data.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"11 1","pages":"19"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85333319","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}
In this demo, we present a novel method to estimate joint angles and distance traveled by a human while walking. Understanding the kinematics of the human leg gives the velocities associated with forward human motion. Gyroscopes and accelerometers placed at two limbs provide the required measurement inputs. The inputs are used to estimate the desired state parameters associated with forward motion using a constrained Kalman Filter. Experimental results with walking subjects show that distance walked can be measured with accuracy comparable to state of the art motion tracking systems. The average RMSE is 0.05 meters per stride, which corresponds to 95% accuracy considering average stride length of 1 metre from the experiments.
在这个演示中，我们提出了一种新的方法来估计人类行走时的关节角度和距离。了解了人腿的运动学，我们就知道了人向前运动的速度。安装在四肢上的陀螺仪和加速度计提供所需的测量输入。输入用于使用约束卡尔曼滤波器估计与前向运动相关的期望状态参数。以行走为实验对象的实验结果表明，行走距离的测量精度可与最先进的运动跟踪系统相媲美。平均RMSE为0.05 m /跨步，考虑实验中平均跨步长度为1 m，准确率为95%。
{"title":"Modeling human gait using a Kalman filter to measure walking distance","authors":"K. Nagarajan, N. Gans, R. Jafari","doi":"10.1145/2077546.2077584","DOIUrl":"https://doi.org/10.1145/2077546.2077584","url":null,"abstract":"In this demo, we present a novel method to estimate joint angles and distance traveled by a human while walking. Understanding the kinematics of the human leg gives the velocities associated with forward human motion. Gyroscopes and accelerometers placed at two limbs provide the required measurement inputs. The inputs are used to estimate the desired state parameters associated with forward motion using a constrained Kalman Filter. Experimental results with walking subjects show that distance walked can be measured with accuracy comparable to state of the art motion tracking systems. The average RMSE is 0.05 meters per stride, which corresponds to 95% accuracy considering average stride length of 1 metre from the experiments.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"26 1","pages":"34"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83285093","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}
Wearable thermoelectric generator (WTEG) technology is a unique energy harvesting application currently being developed by Perpetua Power Source Technologies for powering low-power transceivers and physiological monitoring sensors using body heat as an always-available power source. Integrated into wearable structures, such as an armband, clothing patch or directly embedded into a low-power wireless monitoring device, WTEGs utilize heat from the body and convert it into electrical energy. WTEG technology can be used to renewably and reliably power on-body sensors that can wirelessly monitor an individual's location or a specific physiological condition.
可穿戴热电发电机(WTEG)技术是一种独特的能量收集应用，目前由Perpetua Power Source Technologies开发，用于为低功耗收发器和生理监测传感器供电，使用体热作为始终可用的电源。wteg集成到可穿戴结构中，如臂章、衣服贴片或直接嵌入到低功耗无线监控设备中，利用身体的热量并将其转换为电能。WTEG技术可用于可再生和可靠地为身体传感器供电，这些传感器可以无线监测个人的位置或特定的生理状况。
{"title":"Converting body heat into reliable energy for powering physiological wireless sensors","authors":"I. Stark","doi":"10.1145/2077546.2077580","DOIUrl":"https://doi.org/10.1145/2077546.2077580","url":null,"abstract":"Wearable thermoelectric generator (WTEG) technology is a unique energy harvesting application currently being developed by Perpetua Power Source Technologies for powering low-power transceivers and physiological monitoring sensors using body heat as an always-available power source.\u0000 Integrated into wearable structures, such as an armband, clothing patch or directly embedded into a low-power wireless monitoring device, WTEGs utilize heat from the body and convert it into electrical energy. WTEG technology can be used to renewably and reliably power on-body sensors that can wirelessly monitor an individual's location or a specific physiological condition.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"23 1","pages":"31"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85840403","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}
Robert F. Dickerson, T. Hnat, Enamul Hoque, J. Stankovic
We demonstrate a subset of the components used in a real-time depression monitoring product for the home. This system runs 24/7 and can potentially detect the early signs of a depression episode, as well as track progress managing a depressive illness. In the complete system, a cohesive set of integrated wireless sensors, a touch screen station, and associated software deliver the above capabilities. The data collected are multi-modal, spanning a number of different behavioral domains including sleep, weight, activities of daily living, and speech prosody. The reports generated by this aggregated data across multiple behavioral domains are aimed to provide caregivers with more accurate and thorough information about the patient's current functioning, thus helping in their diagnostic assessment and therapeutic treatment planning as well as for patients in the management and tracking of their symptoms. We show how the sleep monitoring module can collect bed movements to infer sleeping times and periods of restlessness, and we also present the caregiver display with its series of reports of patient emotional health.
{"title":"Demonstration of sleep monitoring and caregiver displays for depression monitoring","authors":"Robert F. Dickerson, T. Hnat, Enamul Hoque, J. Stankovic","doi":"10.1145/2077546.2077571","DOIUrl":"https://doi.org/10.1145/2077546.2077571","url":null,"abstract":"We demonstrate a subset of the components used in a real-time depression monitoring product for the home. This system runs 24/7 and can potentially detect the early signs of a depression episode, as well as track progress managing a depressive illness. In the complete system, a cohesive set of integrated wireless sensors, a touch screen station, and associated software deliver the above capabilities. The data collected are multi-modal, spanning a number of different behavioral domains including sleep, weight, activities of daily living, and speech prosody. The reports generated by this aggregated data across multiple behavioral domains are aimed to provide caregivers with more accurate and thorough information about the patient's current functioning, thus helping in their diagnostic assessment and therapeutic treatment planning as well as for patients in the management and tracking of their symptoms. We show how the sleep monitoring module can collect bed movements to infer sleeping times and periods of restlessness, and we also present the caregiver display with its series of reports of patient emotional health.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"39 1","pages":"22"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90592405","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}
Md. Mahbubur Rahman, A. Ali, K. Plarre, M. al’Absi, Emre Ertin, Santosh Kumar
Automated detection of social interactions in the natural environment has resulted in promising advances in organizational behavior, consumer behavior, and behavioral health. Progress, however, has been limited since the primary means of assessing social interactions today (i.e., audio recording) has several issues in field usage such as microphone occlusion, lack of speaker specificity, and high energy drain, in addition to significant privacy concerns. In this paper, we present mConverse, a new mobile-based system to infer conversation episodes from respiration measurements collected in the field from an unobtrusively wearable respiratory inductive plethysmograph (RIP) band worn around the user's chest. The measurements are wire-lessly transmitted to a mobile phone, where they are used in a novel machine learning model to determine whether the wearer is speaking, listening, or quiet. Our model incorporates several innovations to address issues that naturally arise in the noisy field environment such as confounding events, poor data quality due to sensor loosening and detachment, losses in the wireless channel, etc. Our basic model obtains 83% accuracy for the three class classification. We formulate a Hidden Markov Model to further improve the accuracy to 87%. Finally, we apply our model to data collected from 22 subjects who wore the sensor for 2 full days in the field to observe conversation behavior in daily life and find that people spend 25% of their day in conversations.
{"title":"mConverse: inferring conversation episodes from respiratory measurements collected in the field","authors":"Md. Mahbubur Rahman, A. Ali, K. Plarre, M. al’Absi, Emre Ertin, Santosh Kumar","doi":"10.1145/2077546.2077557","DOIUrl":"https://doi.org/10.1145/2077546.2077557","url":null,"abstract":"Automated detection of social interactions in the natural environment has resulted in promising advances in organizational behavior, consumer behavior, and behavioral health. Progress, however, has been limited since the primary means of assessing social interactions today (i.e., audio recording) has several issues in field usage such as microphone occlusion, lack of speaker specificity, and high energy drain, in addition to significant privacy concerns.\u0000 In this paper, we present mConverse, a new mobile-based system to infer conversation episodes from respiration measurements collected in the field from an unobtrusively wearable respiratory inductive plethysmograph (RIP) band worn around the user's chest. The measurements are wire-lessly transmitted to a mobile phone, where they are used in a novel machine learning model to determine whether the wearer is speaking, listening, or quiet. Our model incorporates several innovations to address issues that naturally arise in the noisy field environment such as confounding events, poor data quality due to sensor loosening and detachment, losses in the wireless channel, etc. Our basic model obtains 83% accuracy for the three class classification. We formulate a Hidden Markov Model to further improve the accuracy to 87%. Finally, we apply our model to data collected from 22 subjects who wore the sensor for 2 full days in the field to observe conversation behavior in daily life and find that people spend 25% of their day in conversations.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"35 1","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89391424","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}
In this paper, we present an overview of the VITFIZ platform. VITFIZ is a mobile exercise system which we have developed for the provision of personalized feedback to patients performing rehabilitation exercise. VITFIZ has been developed in response to the need for novel solutions that will facilitate effective implementation and management of rehabilitation exercise for patients in the home setting between visits to the clinic. Increased availability of smart phones equipped with motion sensors means that the system can be deployed on a mobile platform. VITFIZ has been evaluated in the laboratory and clinical setting and initial results suggest that it is an effective tool for increasing accuracy of exercise technique and motivation to perform exercise. It has promise as a mobile health application for the rehabilitation sector
{"title":"Rehabilitation exercise feedback on Android platform","authors":"B. Caulfield, Jason Blood, Barry Smyth, D. Kelly","doi":"10.1145/2077546.2077567","DOIUrl":"https://doi.org/10.1145/2077546.2077567","url":null,"abstract":"In this paper, we present an overview of the VITFIZ platform. VITFIZ is a mobile exercise system which we have developed for the provision of personalized feedback to patients performing rehabilitation exercise. VITFIZ has been developed in response to the need for novel solutions that will facilitate effective implementation and management of rehabilitation exercise for patients in the home setting between visits to the clinic. Increased availability of smart phones equipped with motion sensors means that the system can be deployed on a mobile platform. VITFIZ has been evaluated in the laboratory and clinical setting and initial results suggest that it is an effective tool for increasing accuracy of exercise technique and motivation to perform exercise. It has promise as a mobile health application for the rehabilitation sector","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"65 1","pages":"18"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76521631","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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