Muhammad Farooq, P. Chandler-Laney, M. Hernandez-reif, E. Sazonov
Research shows that rapid weight gain in infancy is associated to the development of obesity at a later stage in life. Feeding behavior in infants contributes to the rapid weight in early life. Sucking counts can be used to quantify the feeding behavior in infants. This paper presents a new signal processing algorithm to estimate sucking counts in infants from the data collected by a wireless jaw motion sensor. Meals for both breast-fed and bottle-fed infants were videotaped and synchronized with the sensor signal. Sensor signals were normalized and divided into 10 second segments. A percentile-based peak detection algorithm was used to estimate sucking count for each segment. The proposed approach was able to achieve a mean absolute error rate of 7.11% compared to human annotated sucking count with an average intra-class correlation of 0.92 between the algorithm and human raters.
{"title":"A wireless sensor system for quantification of infant feeding behavior","authors":"Muhammad Farooq, P. Chandler-Laney, M. Hernandez-reif, E. Sazonov","doi":"10.1145/2811780.2811934","DOIUrl":"https://doi.org/10.1145/2811780.2811934","url":null,"abstract":"Research shows that rapid weight gain in infancy is associated to the development of obesity at a later stage in life. Feeding behavior in infants contributes to the rapid weight in early life. Sucking counts can be used to quantify the feeding behavior in infants. This paper presents a new signal processing algorithm to estimate sucking counts in infants from the data collected by a wireless jaw motion sensor. Meals for both breast-fed and bottle-fed infants were videotaped and synchronized with the sensor signal. Sensor signals were normalized and divided into 10 second segments. A percentile-based peak detection algorithm was used to estimate sucking count for each segment. The proposed approach was able to achieve a mean absolute error rate of 7.11% compared to human annotated sucking count with an average intra-class correlation of 0.92 between the algorithm and human raters.","PeriodicalId":102963,"journal":{"name":"Proceedings of the conference on Wireless Health","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121257147","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}
Mood has been systematically studied by psychologists for over 100 years. As mood is a subjective feeling, any study of mood must take into account and accurately capture user's perception of an experienced feeling. In last 40 years, a number of pen-and-paper mood self-assessment scales have been proposed. Typically, a person is asked to separately rate various dimensions of the experienced feeling (e.g., pleasure and arousal) or mood items (interested, agitated, excited, etc.) on numeric scales (e.g., between 0 and 10). These partial ratings are then combined into an overall mood rating (or into its positive and negative affect). Pen-and-paper mood scales are used in basic research on mood and in clinical practice. Mobile technology makes it possible to extend mood self-assessment from lab to real life rather, collecting mood data frequently, over long time, in variety of life situations. With these motivations, we developed mobile versions of validated pen-and-paper scales for mood self-assessment to facilitate accurate in-situ mood self-assessment in real-life situations by smartphone users. The novelty of our Mobile Mood Scales (MMS) app is the use of visual effects such as color, changing brightness, animation and photos. We believe these mobile-technology-enabled aids involving user's senses can make mood self-assessment more intuitive and engaging for users than pen-and-paper mood scales that rely on linguistic terms and numerical rating. We built a customization layer that allows a doctor to generate a required mood app by selecting the mood scale required (e.g., PANAS or SPANE) as well as specific optional features such as the granularity of a rating scale (e.g., 5-point scale with radio buttons) and visual effects. In an evaluation survey, 61% of 48 participants found special features such as use of color, brightness and photos helpful in reflecting on own mood. 83% of 48 participants preferred mobile mood scales over pen-and-paper scales. We received encouraging feedback from the designers of original pen-and-paper mood scales. We envision applications of MMS in psychological studies of mood, in monitoring the efficacy of medical interventions and medication, as a component for mHealth apps where it is important to know fluctuations of patient's mood.
{"title":"Mood self-assessment on smartphones","authors":"Le Minh Khue, Eng Lieh Ouh, S. Jarzabek","doi":"10.1145/2811780.2811921","DOIUrl":"https://doi.org/10.1145/2811780.2811921","url":null,"abstract":"Mood has been systematically studied by psychologists for over 100 years. As mood is a subjective feeling, any study of mood must take into account and accurately capture user's perception of an experienced feeling. In last 40 years, a number of pen-and-paper mood self-assessment scales have been proposed. Typically, a person is asked to separately rate various dimensions of the experienced feeling (e.g., pleasure and arousal) or mood items (interested, agitated, excited, etc.) on numeric scales (e.g., between 0 and 10). These partial ratings are then combined into an overall mood rating (or into its positive and negative affect). Pen-and-paper mood scales are used in basic research on mood and in clinical practice. Mobile technology makes it possible to extend mood self-assessment from lab to real life rather, collecting mood data frequently, over long time, in variety of life situations. With these motivations, we developed mobile versions of validated pen-and-paper scales for mood self-assessment to facilitate accurate in-situ mood self-assessment in real-life situations by smartphone users. The novelty of our Mobile Mood Scales (MMS) app is the use of visual effects such as color, changing brightness, animation and photos. We believe these mobile-technology-enabled aids involving user's senses can make mood self-assessment more intuitive and engaging for users than pen-and-paper mood scales that rely on linguistic terms and numerical rating. We built a customization layer that allows a doctor to generate a required mood app by selecting the mood scale required (e.g., PANAS or SPANE) as well as specific optional features such as the granularity of a rating scale (e.g., 5-point scale with radio buttons) and visual effects. In an evaluation survey, 61% of 48 participants found special features such as use of color, brightness and photos helpful in reflecting on own mood. 83% of 48 participants preferred mobile mood scales over pen-and-paper scales. We received encouraging feedback from the designers of original pen-and-paper mood scales. We envision applications of MMS in psychological studies of mood, in monitoring the efficacy of medical interventions and medication, as a component for mHealth apps where it is important to know fluctuations of patient's mood.","PeriodicalId":102963,"journal":{"name":"Proceedings of the conference on Wireless Health","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122881396","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, by making use of virtual reality technology, motion sensors and cloud computing platform, we propose a cloud-based virtual training system for physical therapy, which enables a user to be trained by following a pre-recorded avatar instructor and getting real-time guidance using mobile device through wireless network. To evaluate the user's performance, we compare the motion data of the user and the pre-recoded avatar instructor. However, human reaction delay and network delay cause the data misalignment problem in the proposed cloud-based virtual training system. To align the motion data and evaluate the user's performance, we use Dynamic Time Warping (DTW) to calculate the similarity between the two sequences. Moreover, we propose a variant of the DTW algorithm we term Gesture-Based Dynamic Time Warping (GB-DTW) which segments the whole motion sequence and provides evaluation score for each gesture in real time. Experiments with multiple subjects under real network condition show that the proposed GB-DTW algorithm performs much better than other evaluation methods. To help the user calibrate his movements, the proposed system also provides visual and textual guidance for the user.
{"title":"Motion data alignment and real-time guidance in cloud-based virtual training system","authors":"Wenchuan Wei, Yao Lu, Catherine D. Printz, S. Dey","doi":"10.1145/2811780.2811952","DOIUrl":"https://doi.org/10.1145/2811780.2811952","url":null,"abstract":"In this paper, by making use of virtual reality technology, motion sensors and cloud computing platform, we propose a cloud-based virtual training system for physical therapy, which enables a user to be trained by following a pre-recorded avatar instructor and getting real-time guidance using mobile device through wireless network. To evaluate the user's performance, we compare the motion data of the user and the pre-recoded avatar instructor. However, human reaction delay and network delay cause the data misalignment problem in the proposed cloud-based virtual training system. To align the motion data and evaluate the user's performance, we use Dynamic Time Warping (DTW) to calculate the similarity between the two sequences. Moreover, we propose a variant of the DTW algorithm we term Gesture-Based Dynamic Time Warping (GB-DTW) which segments the whole motion sequence and provides evaluation score for each gesture in real time. Experiments with multiple subjects under real network condition show that the proposed GB-DTW algorithm performs much better than other evaluation methods. To help the user calibrate his movements, the proposed system also provides visual and textual guidance for the user.","PeriodicalId":102963,"journal":{"name":"Proceedings of the conference on Wireless Health","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127587734","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}
J. Hipp, Alicia Manteiga, Amanda Burgess, Abby Stylianou, Robert Pless
Active transportation is an important contributor to physical activity. Understanding active transportation trends and transportation mode share is important to public health research and city planners. Objective measurement of active transportation can be costly and time-consuming, and existing camera-based algorithms, while developing, are functionally limited to specific settings and distances. In this study, 28,992 publicly available webcam images from two intersections in Washington, D.C., were used to establish trends in active transportation. Amazon Mechanical Turk workers were found to be reliable identifiers of pedestrian and vehicular activity, data validated against trained research assistant image annotation. Webcam and crowdsource annotation provides a cost-effective alternative to traditional objective measures of active transportation and mode share through the use of publicly available wireless webcams. Additional research is needed to expand the utility and external validity of publicly available imaged-based active transportation methodology and image annotation.
{"title":"Cameras and crowds in transportation tracking","authors":"J. Hipp, Alicia Manteiga, Amanda Burgess, Abby Stylianou, Robert Pless","doi":"10.1145/2811780.2811941","DOIUrl":"https://doi.org/10.1145/2811780.2811941","url":null,"abstract":"Active transportation is an important contributor to physical activity. Understanding active transportation trends and transportation mode share is important to public health research and city planners. Objective measurement of active transportation can be costly and time-consuming, and existing camera-based algorithms, while developing, are functionally limited to specific settings and distances. In this study, 28,992 publicly available webcam images from two intersections in Washington, D.C., were used to establish trends in active transportation. Amazon Mechanical Turk workers were found to be reliable identifiers of pedestrian and vehicular activity, data validated against trained research assistant image annotation. Webcam and crowdsource annotation provides a cost-effective alternative to traditional objective measures of active transportation and mode share through the use of publicly available wireless webcams. Additional research is needed to expand the utility and external validity of publicly available imaged-based active transportation methodology and image annotation.","PeriodicalId":102963,"journal":{"name":"Proceedings of the conference on Wireless Health","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133422030","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}
Jiaqi Gong, K. Rose, I. Emi, J. Specht, Enamul Hoque, Dawei Fan, Sriram Raju Dandu, Robert F. Dickerson, Y. Perkhounkova, J. Lach, J. Stankovic
Patients with Alzheimer's Disease (AD) often experience urinary incontinence and agitation during sleep. There is some evidence that these phenomena are related, but the relationships (and the subsequent opportunity for caregiver intervention) has never been formally studied. In this work, the relationships among the times of occurrence of nighttime agitation, sleep continuity and duration, and urinary incontinence are identified for persons with AD by using innovative, non-invasive technology. Deployments in 12 homes demonstrate both the utility of the technical monitoring system and the discovered correlations between agitation and incontinence for these 12 AD patients. Implications of possible interventions are discussed. Lessons learned for technical, non-technical and health care implications are presented.
{"title":"Home wireless sensing system for monitoring nighttime agitation and incontinence in patients with Alzheimer's disease","authors":"Jiaqi Gong, K. Rose, I. Emi, J. Specht, Enamul Hoque, Dawei Fan, Sriram Raju Dandu, Robert F. Dickerson, Y. Perkhounkova, J. Lach, J. Stankovic","doi":"10.1145/2811780.2822324","DOIUrl":"https://doi.org/10.1145/2811780.2822324","url":null,"abstract":"Patients with Alzheimer's Disease (AD) often experience urinary incontinence and agitation during sleep. There is some evidence that these phenomena are related, but the relationships (and the subsequent opportunity for caregiver intervention) has never been formally studied. In this work, the relationships among the times of occurrence of nighttime agitation, sleep continuity and duration, and urinary incontinence are identified for persons with AD by using innovative, non-invasive technology. Deployments in 12 homes demonstrate both the utility of the technical monitoring system and the discovered correlations between agitation and incontinence for these 12 AD patients. Implications of possible interventions are discussed. Lessons learned for technical, non-technical and health care implications are presented.","PeriodicalId":102963,"journal":{"name":"Proceedings of the conference on Wireless Health","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125314346","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}
Systems for measuring Activities of Daily Livings (ADL) play a significant role in home health-care. The ability of performing ADLs successfully is used as an important factor in deciding treatments and services for patients and elderly citizens. However, most of these systems are designed for single-resident homes. The presence of multiple people creates higher numbers of parallel and overlapping activities, and introduces additional complexities in defining and recognizing activity instances. We present SARRIMA, a system that recognizes activity instances and assigns those activities to a person in 2-resident homes using only passive sensors. We evaluate the efficiency of SARRIMA in two different public datasets (data from real homes) with multiple residents. On the average SARRIMA detects more than 97% of the activity instances. We also show how the person assignment accuracy varies as a function of the similarity of behavior of the 2 people living together and of the types of passive sensors installed.
{"title":"SARRIMA: smart ADL recognizer and resident identifier in multi-resident accommodations","authors":"I. Emi, J. Stankovic","doi":"10.1145/2811780.2811916","DOIUrl":"https://doi.org/10.1145/2811780.2811916","url":null,"abstract":"Systems for measuring Activities of Daily Livings (ADL) play a significant role in home health-care. The ability of performing ADLs successfully is used as an important factor in deciding treatments and services for patients and elderly citizens. However, most of these systems are designed for single-resident homes. The presence of multiple people creates higher numbers of parallel and overlapping activities, and introduces additional complexities in defining and recognizing activity instances. We present SARRIMA, a system that recognizes activity instances and assigns those activities to a person in 2-resident homes using only passive sensors. We evaluate the efficiency of SARRIMA in two different public datasets (data from real homes) with multiple residents. On the average SARRIMA detects more than 97% of the activity instances. We also show how the person assignment accuracy varies as a function of the similarity of behavior of the 2 people living together and of the types of passive sensors installed.","PeriodicalId":102963,"journal":{"name":"Proceedings of the conference on Wireless Health","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125335152","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}
Generative model based resource efficient monitoring is an emerging data collection technique that has been shown to have compression ratio of around 40 in simulation environment on medical grade data from MIT BIH database. This paper discusses the intermediate outcomes of an ongoing clinical study where GeMREM enabled sensors are deployed on 125 subjects at the St Luke's cardiac hospital. According to the data from 25 patients we see that GeMREM achieves a compression ratio of 33, the reduction attributed to motion artifacts. We also compare the diagnostic accuracy of GeMREM with compressive sensing (CS) based ECG monitoring techniques. The results show that GeMREM although has better resource efficiency, CS is more accurate in representing temporal parameters such as heart rate, standard deviation of heart rate, and heart rate variability. However, interestingly, GeMREM is more accurate in preserving the shape of an ECG beat. Usage of dual basis in CS also cannot achieve shape accuracy comparable to GeMREM. Further, the reconstruction algorithm for GeMREM is almost 20 times faster than that for CS techniques.
{"title":"Clinical evaluation of generative model based monitoring and comparison with compressive sensing","authors":"Ayan Banerjee, S. Gupta","doi":"10.1145/2811780.2811946","DOIUrl":"https://doi.org/10.1145/2811780.2811946","url":null,"abstract":"Generative model based resource efficient monitoring is an emerging data collection technique that has been shown to have compression ratio of around 40 in simulation environment on medical grade data from MIT BIH database. This paper discusses the intermediate outcomes of an ongoing clinical study where GeMREM enabled sensors are deployed on 125 subjects at the St Luke's cardiac hospital. According to the data from 25 patients we see that GeMREM achieves a compression ratio of 33, the reduction attributed to motion artifacts. We also compare the diagnostic accuracy of GeMREM with compressive sensing (CS) based ECG monitoring techniques. The results show that GeMREM although has better resource efficiency, CS is more accurate in representing temporal parameters such as heart rate, standard deviation of heart rate, and heart rate variability. However, interestingly, GeMREM is more accurate in preserving the shape of an ECG beat. Usage of dual basis in CS also cannot achieve shape accuracy comparable to GeMREM. Further, the reconstruction algorithm for GeMREM is almost 20 times faster than that for CS techniques.","PeriodicalId":102963,"journal":{"name":"Proceedings of the conference on Wireless Health","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117069528","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}
Hua-I Chang, Vivek Desai, O. Santana, Matthew Dempsey, Anchi Su, John Goodlad, Faraz Aghazadeh, G. Pottie
Sensor misplacement is a common obstacle that prevents inertial-based technology from providing reliable motion inference. Traditional approaches require certain calibration postures or activities to be performed. However, this may not be feasible for patients with mobility impairments. We propose a system that uses the Kinect's measurement as the ground truth to opportunistically detect and compensate for such errors. The goal of this study is to provide reliable motion data without the requirement of calibration activities or careful placement of the wearable sensors. First, we identified the instances where the Kinect had an unobstructed view of the limb of interest, and collected data for calibration. Then, we applied double exponential smoothing on the Kinect's position data and performed differentiation twice to generate virtual accelerations. By examining the acceleration vectors from the Kinect and inertial measurement unit (IMU) sensor, the misplacement of IMU sensors can be identified and thus compensated. Our results showed that the calibration algorithms successfully detected orientation error and provided accurate compensation. We also present an example of trajectory reconstruction with misplaced sensors and applied the proposed method. We obtained good agreement of reconstructed trajectories between the rectified sensor and the correctly placed sensor. The outcomes of this research will simplify ground-truth collection in the clinic, and provide reliable inference of motion data in the community.
{"title":"Opportunistic calibration of sensor orientation using the Kinect and inertial measurement unit sensor fusion","authors":"Hua-I Chang, Vivek Desai, O. Santana, Matthew Dempsey, Anchi Su, John Goodlad, Faraz Aghazadeh, G. Pottie","doi":"10.1145/2811780.2811927","DOIUrl":"https://doi.org/10.1145/2811780.2811927","url":null,"abstract":"Sensor misplacement is a common obstacle that prevents inertial-based technology from providing reliable motion inference. Traditional approaches require certain calibration postures or activities to be performed. However, this may not be feasible for patients with mobility impairments. We propose a system that uses the Kinect's measurement as the ground truth to opportunistically detect and compensate for such errors. The goal of this study is to provide reliable motion data without the requirement of calibration activities or careful placement of the wearable sensors. First, we identified the instances where the Kinect had an unobstructed view of the limb of interest, and collected data for calibration. Then, we applied double exponential smoothing on the Kinect's position data and performed differentiation twice to generate virtual accelerations. By examining the acceleration vectors from the Kinect and inertial measurement unit (IMU) sensor, the misplacement of IMU sensors can be identified and thus compensated. Our results showed that the calibration algorithms successfully detected orientation error and provided accurate compensation. We also present an example of trajectory reconstruction with misplaced sensors and applied the proposed method. We obtained good agreement of reconstructed trajectories between the rectified sensor and the correctly placed sensor. The outcomes of this research will simplify ground-truth collection in the clinic, and provide reliable inference of motion data in the community.","PeriodicalId":102963,"journal":{"name":"Proceedings of the conference on Wireless Health","volume":"347 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123186122","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}
Mark Di Sano, Andres A. Perez, M. Labrador, P. Athilingam, F. Giovannetti
Congestive Heart Failure (CHF), by its very nature, may lead to frequent hospital visits due to the complexity of managing the risk factors associated with it. Prescribed treatments for discharged patients are usually a combination of medicine, life style changing guidelines, and physical therapy. Treatment compliance is usually challenging and frustrating for both patients and providers. HeartMapp provides a multi-dimensional approach to address these issues combining patient engagement techniques, remote physiological monitoring, automation of traditional clinical protocols, and clinical decision support, all in one patient centered, self-care mobile application.
{"title":"HeartMapp: a mobile application to improve CHF outcomes and reduce hospital readmissions","authors":"Mark Di Sano, Andres A. Perez, M. Labrador, P. Athilingam, F. Giovannetti","doi":"10.1145/2811780.2811914","DOIUrl":"https://doi.org/10.1145/2811780.2811914","url":null,"abstract":"Congestive Heart Failure (CHF), by its very nature, may lead to frequent hospital visits due to the complexity of managing the risk factors associated with it. Prescribed treatments for discharged patients are usually a combination of medicine, life style changing guidelines, and physical therapy. Treatment compliance is usually challenging and frustrating for both patients and providers. HeartMapp provides a multi-dimensional approach to address these issues combining patient engagement techniques, remote physiological monitoring, automation of traditional clinical protocols, and clinical decision support, all in one patient centered, self-care mobile application.","PeriodicalId":102963,"journal":{"name":"Proceedings of the conference on Wireless Health","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114150294","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}
Wireless motion sensors offer a non-invasive, low cost solution for daily human activity monitoring, which is critically important for the diagnosis and rehabilitation of neurological diseases. However, an accurate and robust estimation method for the step length, one of the most important lower body metrics, is still lacking. In order to tackle this problem, we developed a new robust step length estimation system called the Pose Invariant (PI) method using ankle mounted motion sensors. Using the inverted pendulum model, the traveling distance within each step can be calculated by multiplying the leg length and the sine of the leg's orientation change within each step. Walking data from 9 adult subjects was collected and processed to validate this method. On average, a 3.69% absolute error rate was achieved. In addition, the robustness of this method compared to the non-ZUPT method was shown by an additional experiment over 3 adult subjects.
{"title":"A robust step length estimation system for human gait using motion sensors","authors":"Xiaoxu Wu, Yan Wang, G. Pottie","doi":"10.1145/2811780.2811919","DOIUrl":"https://doi.org/10.1145/2811780.2811919","url":null,"abstract":"Wireless motion sensors offer a non-invasive, low cost solution for daily human activity monitoring, which is critically important for the diagnosis and rehabilitation of neurological diseases. However, an accurate and robust estimation method for the step length, one of the most important lower body metrics, is still lacking. In order to tackle this problem, we developed a new robust step length estimation system called the Pose Invariant (PI) method using ankle mounted motion sensors. Using the inverted pendulum model, the traveling distance within each step can be calculated by multiplying the leg length and the sine of the leg's orientation change within each step. Walking data from 9 adult subjects was collected and processed to validate this method. On average, a 3.69% absolute error rate was achieved. In addition, the robustness of this method compared to the non-ZUPT method was shown by an additional experiment over 3 adult subjects.","PeriodicalId":102963,"journal":{"name":"Proceedings of the conference on Wireless Health","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115170862","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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