Pub Date : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248710
Y. S. Lee, C. Garfield, H. N. Kim
Transitioning a Very Low Birth Weight (VLBW) premature infant from the Neonatal Intensive Care Unit (NICU) to home is a very stressful task for parents. Few studies examined the needs of parents of VLBW infants during the transition; moreover, even less is known about technology development strategies that aim to increase the parenting confidence. In this study, we used Bandura's self-efficacy theory as a framework to understand ways to develop successful interventions for parents of VLBW infants. The self-efficacy theory posits that parenting behavior and the quality of care can be improved by supporting the four major sources of self-efficacy: mastery experiences, vicarious experiences, social persuasion, and physiological responses. We describe self-efficacy theory and its role in the development of technology interventions to support parents of NICU infants using a case study, called NICU-2-HOME.
{"title":"Self-efficacy theory as a framework for interventions that support parents of NICU infants","authors":"Y. S. Lee, C. Garfield, H. N. Kim","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248710","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248710","url":null,"abstract":"Transitioning a Very Low Birth Weight (VLBW) premature infant from the Neonatal Intensive Care Unit (NICU) to home is a very stressful task for parents. Few studies examined the needs of parents of VLBW infants during the transition; moreover, even less is known about technology development strategies that aim to increase the parenting confidence. In this study, we used Bandura's self-efficacy theory as a framework to understand ways to develop successful interventions for parents of VLBW infants. The self-efficacy theory posits that parenting behavior and the quality of care can be improved by supporting the four major sources of self-efficacy: mastery experiences, vicarious experiences, social persuasion, and physiological responses. We describe self-efficacy theory and its role in the development of technology interventions to support parents of NICU infants using a case study, called NICU-2-HOME.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129552708","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 : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248731
Erik E. Stone, M. Skubic
In-home gait measurement results from the apartments of seven older adults obtained using an environmentally mounted depth camera, the Microsoft Kinect, are presented. Previous work evaluating the use of the Kinect for in-home gait assessment in a lab setting has shown the potential of this approach. In this work, a single Kinect sensor and computer have been deployed in five apartments, two of which contain multiple residents, in an independent living facility for older adults. Data collected in the five apartments, along with techniques for generating automated gait measurements from the data, are presented.
{"title":"Passive, in-home gait measurement using an inexpensive depth camera: Initial results","authors":"Erik E. Stone, M. Skubic","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248731","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248731","url":null,"abstract":"In-home gait measurement results from the apartments of seven older adults obtained using an environmentally mounted depth camera, the Microsoft Kinect, are presented. Previous work evaluating the use of the Kinect for in-home gait assessment in a lab setting has shown the potential of this approach. In this work, a single Kinect sensor and computer have been deployed in five apartments, two of which contain multiple residents, in an independent living facility for older adults. Data collected in the five apartments, along with techniques for generating automated gait measurements from the data, are presented.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129647584","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 : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248693
D. U. Khan, K. Siek, S. Ananthanarayan
We conducted a study with eight low socioeconomic status caregivers, the “gatekeepers” to family and community health, to explore their technology perceptions and visualizations that can assist them in managing their families' health. We studied how to visualize the everyday health habits of a population that is at risk for chronic illness. Through semi-structured interviews and prototype design activities, we found that the caregivers wanted to use mobile technology to monitor a subset of their family members for at least one health metric. We inform future work with an analysis of the target population's needs in relation to longitudinal health visualizations with multiple family member data streams.
{"title":"Towards designing health monitoring interfaces for low socioeconomic status families","authors":"D. U. Khan, K. Siek, S. Ananthanarayan","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248693","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248693","url":null,"abstract":"We conducted a study with eight low socioeconomic status caregivers, the “gatekeepers” to family and community health, to explore their technology perceptions and visualizations that can assist them in managing their families' health. We studied how to visualize the everyday health habits of a population that is at risk for chronic illness. Through semi-structured interviews and prototype design activities, we found that the caregivers wanted to use mobile technology to monitor a subset of their family members for at least one health metric. We inform future work with an analysis of the target population's needs in relation to longitudinal health visualizations with multiple family member data streams.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132380394","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 : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248761
Laura R. Pina, Ernesto Ramirez, W. Griswold
Self-tracking wearable devices are capable of tracking calorie consumption and inferring physical activity physical activity to support self-awareness and healthy behavior. These devices automatically capture human behavior (such as walking) but do not typically make the user aware detected unhealthy behaviors. Furthermore, these devices cannot intervene in the moment to make users aware they are engaging in unhealthy behavior (such as sitting for a long period of time) and persuade them to correct these unhealthy behaviors (e.g., by taking a break to go for a walk). There is an increasing trend for people with low physical activity occupations to sit for long periods of time, yet research suggests that lengthy sitting, independent of overall physical activity level, increases the risk of weight gain and mortality [1]. We aim to decrease the duration of sedentary bouts in the workplace by detecting when people have been inactive for a long time and then prompting them take a short break from their desks. In this poster we present the design of Fitbit+, a system that realizes this strategy by leveraging Fitbit's near real-time, automated step logging to detect sedentary behavior and then prompt users to take short breaks.
{"title":"Fitbit+: A behavior-based intervention system to reduce sedentary behavior","authors":"Laura R. Pina, Ernesto Ramirez, W. Griswold","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248761","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248761","url":null,"abstract":"Self-tracking wearable devices are capable of tracking calorie consumption and inferring physical activity physical activity to support self-awareness and healthy behavior. These devices automatically capture human behavior (such as walking) but do not typically make the user aware detected unhealthy behaviors. Furthermore, these devices cannot intervene in the moment to make users aware they are engaging in unhealthy behavior (such as sitting for a long period of time) and persuade them to correct these unhealthy behaviors (e.g., by taking a break to go for a walk). There is an increasing trend for people with low physical activity occupations to sit for long periods of time, yet research suggests that lengthy sitting, independent of overall physical activity level, increases the risk of weight gain and mortality [1]. We aim to decrease the duration of sedentary bouts in the workplace by detecting when people have been inactive for a long time and then prompting them take a short break from their desks. In this poster we present the design of Fitbit+, a system that realizes this strategy by leveraging Fitbit's near real-time, automated step logging to detect sedentary behavior and then prompt users to take short breaks.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116091207","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 : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248774
M. Rodríguez, José R. Roa, A. Morán, S. Nava-Muñoz
Several strategies have been identified for designing effective persuasive technologies that encourage people to adopt healthy lifestyle habits. However, there are no general guidelines for implementing these strategies to motivate elders to exercise, neither they have been evaluated to determine how effective they are for the elderly. To design appropriate persuasive technology prone to be adopted by elders, we are following a user-centered approach. In this paper, we report the design and evaluation of an ambient information system for mobile phones, which supports the following strategies for persuasion: abstraction, historical information and reflection, triggers for exercising, and positive and playful reinforcement.
{"title":"Persuasive strategies for motivating elders to exercise","authors":"M. Rodríguez, José R. Roa, A. Morán, S. Nava-Muñoz","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248774","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248774","url":null,"abstract":"Several strategies have been identified for designing effective persuasive technologies that encourage people to adopt healthy lifestyle habits. However, there are no general guidelines for implementing these strategies to motivate elders to exercise, neither they have been evaluated to determine how effective they are for the elderly. To design appropriate persuasive technology prone to be adopted by elders, we are following a user-centered approach. In this paper, we report the design and evaluation of an ambient information system for mobile phones, which supports the following strategies for persuasion: abstraction, historical information and reflection, triggers for exercising, and positive and playful reinforcement.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127675551","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 : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248698
Konrad Tollmar, Frank Bentley, Cristobal Viedma
In this paper we present the Mobile Health Mashups system, a mobile service that collects data from a variety of health and wellbeing sensors and presents significant correlations across sensors in a mobile widget as well as on a mobile web application. We found that long-term correlation data provided users with new insights about systematic wellness trends that they could not make using only the time series graphs provided by the sensor manufacturers. We describe the Mobile Health Mashups system with a focus on analyzing and detailing the technical solution, such as: integration of sensors, how to create correlations between various data sets, and the presentation of the statistical data as feeds and graphs. We will also describe the iterative design process that involved a 2-month field trial, the outcome of this trial, and implications for design of mobile data mashup systems.
在本文中，我们介绍了移动健康mashup系统，这是一种从各种健康和福祉传感器收集数据的移动服务，并在移动小部件和移动web应用程序中呈现传感器之间的显著相关性。我们发现，长期相关数据为用户提供了关于系统健康趋势的新见解，这是他们仅使用传感器制造商提供的时间序列图所无法做到的。我们将介绍Mobile Health Mashups系统，重点分析和详细介绍技术解决方案，例如:传感器的集成、如何在各种数据集之间创建相关性，以及将统计数据表示为提要和图表。我们还将描述涉及2个月现场试验的迭代设计过程、试验结果以及对移动数据混搭系统设计的影响。
{"title":"Mobile Health Mashups: Making sense of multiple streams of wellbeing and contextual data for presentation on a mobile device","authors":"Konrad Tollmar, Frank Bentley, Cristobal Viedma","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248698","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248698","url":null,"abstract":"In this paper we present the Mobile Health Mashups system, a mobile service that collects data from a variety of health and wellbeing sensors and presents significant correlations across sensors in a mobile widget as well as on a mobile web application. We found that long-term correlation data provided users with new insights about systematic wellness trends that they could not make using only the time series graphs provided by the sensor manufacturers. We describe the Mobile Health Mashups system with a focus on analyzing and detailing the technical solution, such as: integration of sensors, how to create correlations between various data sets, and the presentation of the statistical data as feeds and graphs. We will also describe the iterative design process that involved a 2-month field trial, the outcome of this trial, and implications for design of mobile data mashup systems.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134378270","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 : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248714
Chien-Yen Chang, B. Lange, Mi Zhang, S. Koenig, P. Requejo, N. Somboon, A. Sawchuk, A. Rizzo
The use of Virtual Reality technology for developing tools for rehabilitation has attracted significant interest in the physical therapy arena. This paper presents a comparison of motion tracking performance between the low-cost Microsoft Kinect and the high fidelity OptiTrack optical system. Data is collected on six upper limb motor tasks that have been incorporated into a game-based rehabilitation application. The experiment results show that Kinect can achieve competitive motion tracking performance as OptiTrack and provide “pervasive” accessibility that enables patients to take rehabilitation treatment in clinic and home environment.
{"title":"Towards pervasive physical rehabilitation using Microsoft Kinect","authors":"Chien-Yen Chang, B. Lange, Mi Zhang, S. Koenig, P. Requejo, N. Somboon, A. Sawchuk, A. Rizzo","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248714","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248714","url":null,"abstract":"The use of Virtual Reality technology for developing tools for rehabilitation has attracted significant interest in the physical therapy arena. This paper presents a comparison of motion tracking performance between the low-cost Microsoft Kinect and the high fidelity OptiTrack optical system. Data is collected on six upper limb motor tasks that have been incorporated into a game-based rehabilitation application. The experiment results show that Kinect can achieve competitive motion tracking performance as OptiTrack and provide “pervasive” accessibility that enables patients to take rehabilitation treatment in clinic and home environment.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"350 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115465511","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 : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248600
Enamul Hoque, J. Stankovic
We present AALO: a novel Activity recognition system for single person smart homes using Active Learning in the presence of Overlapped activities. AALO applies data mining techniques to cluster in-home sensor firings so that each cluster represents instances of the same activity. Users only need to label each cluster as an activity as opposed to labeling all instances of all activities. Once the clusters are associated to their corresponding activities, our system can recognize future activities. To improve the activity recognition accuracy, our system preprocesses raw sensor data by identifying overlapping activities. The evaluation of activity recognition performance on a 26-day dataset shows that compared to Naive Bayesian (NB), Hidden Markov Model (HMM), and Hidden Semi Markov Model (HSMM) based activity recognition systems, our average time slice error (24.15%) is much lower than NB (53.04%), and similar to HMM (29.97%) and HSMM (26.29%). Thus, our active learning based approach performs as good as the state of the art supervised techniques (HMM and HSMM).
{"title":"AALO: Activity recognition in smart homes using Active Learning in the presence of Overlapped activities","authors":"Enamul Hoque, J. Stankovic","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248600","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248600","url":null,"abstract":"We present AALO: a novel Activity recognition system for single person smart homes using Active Learning in the presence of Overlapped activities. AALO applies data mining techniques to cluster in-home sensor firings so that each cluster represents instances of the same activity. Users only need to label each cluster as an activity as opposed to labeling all instances of all activities. Once the clusters are associated to their corresponding activities, our system can recognize future activities. To improve the activity recognition accuracy, our system preprocesses raw sensor data by identifying overlapping activities. The evaluation of activity recognition performance on a 26-day dataset shows that compared to Naive Bayesian (NB), Hidden Markov Model (HMM), and Hidden Semi Markov Model (HSMM) based activity recognition systems, our average time slice error (24.15%) is much lower than NB (53.04%), and similar to HMM (29.97%) and HSMM (26.29%). Thus, our active learning based approach performs as good as the state of the art supervised techniques (HMM and HSMM).","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121070106","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 : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248685
S. Wagner, T. Toftegaard, O. Bertelsen
The need for a non-intrusive evaluation infrastructure platform to support research on reliable pervasive healthcare in the unsupervised setting is analyzed and challenges and possibilities are identified. A list of requirements is presented and a solution is suggested that would allow researchers to more easily build and evaluate prototypes for measuring and quantifying the use-context of patients using current state-of-the-art biomedical devices in the unsupervised setting. An initial implementation is introduced as the reliable evaluation infrastructure (RELEI) platform. Several research prototypes using the basic RELEI platform are presented to illustrate the purpose, and provide experiences on platform usage.
{"title":"Requirements for an evaluation infrastructure for reliable pervasive healthcare research","authors":"S. Wagner, T. Toftegaard, O. Bertelsen","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248685","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248685","url":null,"abstract":"The need for a non-intrusive evaluation infrastructure platform to support research on reliable pervasive healthcare in the unsupervised setting is analyzed and challenges and possibilities are identified. A list of requirements is presented and a solution is suggested that would allow researchers to more easily build and evaluate prototypes for measuring and quantifying the use-context of patients using current state-of-the-art biomedical devices in the unsupervised setting. An initial implementation is introduced as the reliable evaluation infrastructure (RELEI) platform. Several research prototypes using the basic RELEI platform are presented to illustrate the purpose, and provide experiences on platform usage.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125926767","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 : 2012-05-21DOI: 10.4108/ICST.PERVASIVEHEALTH.2012.248643
Tigest Tamrat, M. Griffin, Sonia Rupcic, S. Kachnowski, Tom Taylor, J. Barfield
Falls are the leading cause of disability and injury-related deaths among older adults, resulting in over 1.6 million annual emergency hospitalizations in the United States. Fall detection devices often rely on dramatized falls when developing algorithms. This study used tri-axial accelerometers worn by older adult research subjects in order to (1) collect false positive data (2) capture potential fall events and (3) evaluate the usability of the device among this target population. Twelve older adults wore activity monitors while participating in structured and unstructured activities. The study collected data on 120 patient days, yielding 492.5 hours of monitored time. Actigraphy data of annotated activities were used to define parameters for refining the algorithm. No falls occurred during the study, but valuable false positive data were collected. The study also obtained information on the usability of the devices and revealed user perspectives on commercializing the final product.
{"title":"Operationalizing a wireless wearable fall detection sensor for older adults","authors":"Tigest Tamrat, M. Griffin, Sonia Rupcic, S. Kachnowski, Tom Taylor, J. Barfield","doi":"10.4108/ICST.PERVASIVEHEALTH.2012.248643","DOIUrl":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2012.248643","url":null,"abstract":"Falls are the leading cause of disability and injury-related deaths among older adults, resulting in over 1.6 million annual emergency hospitalizations in the United States. Fall detection devices often rely on dramatized falls when developing algorithms. This study used tri-axial accelerometers worn by older adult research subjects in order to (1) collect false positive data (2) capture potential fall events and (3) evaluate the usability of the device among this target population. Twelve older adults wore activity monitors while participating in structured and unstructured activities. The study collected data on 120 patient days, yielding 492.5 hours of monitored time. Actigraphy data of annotated activities were used to define parameters for refining the algorithm. No falls occurred during the study, but valuable false positive data were collected. The study also obtained information on the usability of the devices and revealed user perspectives on commercializing the final product.","PeriodicalId":119950,"journal":{"name":"2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129648884","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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