Andres Molina-Markham, Ronald A. Peterson, Joseph Skinner, Tianlong Yun, Bhargav Golla, Kevin Freeman, Travis Peters, Jacob M. Sorber, R. Halter, D. Kotz
Interest in using mobile technologies for health-related applications (mHealth) has increased. However, none of the available mobile platforms provide the essential properties that are needed by these applications. An mHealth platform must be (i) secure; (ii) provide high availability; and (iii) allow for the deployment of multiple third-party mHealth applications that share access to an individual's devices and data. Smartphones may not be able to provide property (ii) because there are activities and situations in which an individual may not be able to carry them (e.g., while in a contact sport). A low-power wearable device can provide higher availability, remaining attached to the user during most activities. Furthermore, some mHealth applications require integrating multiple on-body or near-body devices, some owned by a single individual, but others shared with multiple individuals. In this paper, we propose a secure system architecture for a low-power bracelet that can run multiple applications and manage access to shared resources in a body-area mHealth network. The wearer can install a personalized mix of third-party applications to support the monitoring of multiple medical conditions or wellness goals, with strong security safeguards. Our preliminary implementation and evaluation supports the hypothesis that our approach allows for the implementation of a resource monitor on far less power than would be consumed by a mobile device running Linux or Android. Our preliminary experiments demonstrate that our secure architecture would enable applications to run for several weeks on a small wearable device without recharging.
{"title":"Amulet: a secure architecture for mHealth applications for low-power wearable devices","authors":"Andres Molina-Markham, Ronald A. Peterson, Joseph Skinner, Tianlong Yun, Bhargav Golla, Kevin Freeman, Travis Peters, Jacob M. Sorber, R. Halter, D. Kotz","doi":"10.1145/2676431.2676432","DOIUrl":"https://doi.org/10.1145/2676431.2676432","url":null,"abstract":"Interest in using mobile technologies for health-related applications (mHealth) has increased. However, none of the available mobile platforms provide the essential properties that are needed by these applications. An mHealth platform must be (i) secure; (ii) provide high availability; and (iii) allow for the deployment of multiple third-party mHealth applications that share access to an individual's devices and data. Smartphones may not be able to provide property (ii) because there are activities and situations in which an individual may not be able to carry them (e.g., while in a contact sport). A low-power wearable device can provide higher availability, remaining attached to the user during most activities. Furthermore, some mHealth applications require integrating multiple on-body or near-body devices, some owned by a single individual, but others shared with multiple individuals. In this paper, we propose a secure system architecture for a low-power bracelet that can run multiple applications and manage access to shared resources in a body-area mHealth network. The wearer can install a personalized mix of third-party applications to support the monitoring of multiple medical conditions or wellness goals, with strong security safeguards. Our preliminary implementation and evaluation supports the hypothesis that our approach allows for the implementation of a resource monitor on far less power than would be consumed by a mobile device running Linux or Android. Our preliminary experiments demonstrate that our secure architecture would enable applications to run for several weeks on a small wearable device without recharging.","PeriodicalId":183803,"journal":{"name":"Proceedings of the 1st Workshop on Mobile Medical Applications","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121696760","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}
A sedentary lifestyle is becoming common for many individuals throughout the United States; however, this comes with a health cost of various preventable diseases such as cardiovascular disease, colon cancer, metabolic syndrome, and diabetes. Many times, individuals are completely unaware of how his or her health has deteriorated because of the slow progression or the demands of a job. We seek to bring attention to these problems by identifying specific sedentary activities and propose that just-in-time interventions could be used to help individuals overcome some of these problems. Our solution involves wearable sensors and utilizes a kinematic-based activity recognition systems to identify sedentary and light-intensity activities. Our system is evaluated with a series of laboratory experiments that include data from 34 individuals and a total of over 1400 minutes of activity. Results indicate that our system has a classification accuracy of up to 95.4 percent across all activities.
{"title":"Kinematic-based sedentary and light-intensity activity detection for wearable medical applications","authors":"Kazi I. Zaman, Sami R. Yli-Piipari, T. Hnat","doi":"10.1145/2676431.2676433","DOIUrl":"https://doi.org/10.1145/2676431.2676433","url":null,"abstract":"A sedentary lifestyle is becoming common for many individuals throughout the United States; however, this comes with a health cost of various preventable diseases such as cardiovascular disease, colon cancer, metabolic syndrome, and diabetes. Many times, individuals are completely unaware of how his or her health has deteriorated because of the slow progression or the demands of a job. We seek to bring attention to these problems by identifying specific sedentary activities and propose that just-in-time interventions could be used to help individuals overcome some of these problems. Our solution involves wearable sensors and utilizes a kinematic-based activity recognition systems to identify sedentary and light-intensity activities. Our system is evaluated with a series of laboratory experiments that include data from 34 individuals and a total of over 1400 minutes of activity. Results indicate that our system has a classification accuracy of up to 95.4 percent across all activities.","PeriodicalId":183803,"journal":{"name":"Proceedings of the 1st Workshop on Mobile Medical Applications","volume":"68 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114530029","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}
Dina Najeeb, Antonio Grass, Gladys Garcia, Ryan Debbiny, A. Nahapetian
In this paper, we collect electrical signals emitted from the brain during its normal function, specifically from a single electrode placed over the frontal lobe of the brain, to provide an interface for communication without any physical movement. The systems intended audience includes stroke victims and people with paralysis and other advanced neurologic impairments. The presented MindLogger system is a practical, cost-effective, and noninvasive solution that enables people to select letters, compile words, and create sentences by employing only their electroencephalogram (EEG) activity, alongside existing capabilities in mobile computing.
{"title":"MindLogger: a brain-computer interface for word building using brainwaves","authors":"Dina Najeeb, Antonio Grass, Gladys Garcia, Ryan Debbiny, A. Nahapetian","doi":"10.1145/2676431.2676434","DOIUrl":"https://doi.org/10.1145/2676431.2676434","url":null,"abstract":"In this paper, we collect electrical signals emitted from the brain during its normal function, specifically from a single electrode placed over the frontal lobe of the brain, to provide an interface for communication without any physical movement. The systems intended audience includes stroke victims and people with paralysis and other advanced neurologic impairments. The presented MindLogger system is a practical, cost-effective, and noninvasive solution that enables people to select letters, compile words, and create sentences by employing only their electroencephalogram (EEG) activity, alongside existing capabilities in mobile computing.","PeriodicalId":183803,"journal":{"name":"Proceedings of the 1st Workshop on Mobile Medical Applications","volume":"45 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113944188","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}
Many people are bored with their current physical activities and would like individualized recommendations of alternatives. Even users who have favorite exercises may seek recommendations if their context (e.g., bad weather, location) changes. Prior work has focused on tracking user activities and goal-setting, but not on recommendations. In this paper, we describe RecFit, which systematically suggests physical activities based on the user's context (e.g. risk tolerance, budget, location, weather). RecFit works from 137 activities selected from the 2011 compendium of physical activities in order to recommend the 5 most suitable recommendations for each user. We describe our filtering criteria, algorithms, prototype and RecFit's activity database, which augments activities with metadata of ideal performance context (popularity, sociability, risk, location, expense, time, and weather).
{"title":"RecFit: a context-aware system for recommending physical activities","authors":"Qian He, E. Agu, D. Strong, B. Tulu","doi":"10.1145/2676431.2676439","DOIUrl":"https://doi.org/10.1145/2676431.2676439","url":null,"abstract":"Many people are bored with their current physical activities and would like individualized recommendations of alternatives. Even users who have favorite exercises may seek recommendations if their context (e.g., bad weather, location) changes. Prior work has focused on tracking user activities and goal-setting, but not on recommendations. In this paper, we describe RecFit, which systematically suggests physical activities based on the user's context (e.g. risk tolerance, budget, location, weather). RecFit works from 137 activities selected from the 2011 compendium of physical activities in order to recommend the 5 most suitable recommendations for each user. We describe our filtering criteria, algorithms, prototype and RecFit's activity database, which augments activities with metadata of ideal performance context (popularity, sociability, risk, location, expense, time, and weather).","PeriodicalId":183803,"journal":{"name":"Proceedings of the 1st Workshop on Mobile Medical Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121703532","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}
With the new regulations on mobile medical applications (apps), the FDA has classified mobile apps that use games to motivate patients to perform health-related activities at home (such as physical therapy exercises) as a mobile medical application. Due to it posing lower risk to the public though, the FDA will only exercise enforcement discretion. The question is thus posed--when should apps that promote physical therapy at home for children with motor disabilities, apps in which the therapy is part of their overall rehabilitation protocol, transition to regulatory oversight? Of course this transition will not transpire until therapists begin to rely on data extracted from these apps and use this information to revise the therapy protocol for their patients. This leads to the first issue that should be addressing when dealing with the novel nature of mobile-based healthcare applications--validating the effectiveness of these types of apps. As such, in this paper, we present a pilot study to collect empirical evidence on the effect of a mobile-based healthcare application, designed for children, that is focused on improving motor skills. Results from the protocol, which involved eighty-five participants, show that these types of apps may result in a significant change in motor skills learning. Although the study in this paper involved adult participants, the methods proposed could be adapted by special education teachers and therapists to assess the quality of other such applications used by children in various educational and therapy settings.
{"title":"Pilot study to evaluate the effectiveness of a mobile-based therapy and educational app for children","authors":"A. Howard, J. MacCalla","doi":"10.1145/2668332.2676437","DOIUrl":"https://doi.org/10.1145/2668332.2676437","url":null,"abstract":"With the new regulations on mobile medical applications (apps), the FDA has classified mobile apps that use games to motivate patients to perform health-related activities at home (such as physical therapy exercises) as a mobile medical application. Due to it posing lower risk to the public though, the FDA will only exercise enforcement discretion. The question is thus posed--when should apps that promote physical therapy at home for children with motor disabilities, apps in which the therapy is part of their overall rehabilitation protocol, transition to regulatory oversight? Of course this transition will not transpire until therapists begin to rely on data extracted from these apps and use this information to revise the therapy protocol for their patients. This leads to the first issue that should be addressing when dealing with the novel nature of mobile-based healthcare applications--validating the effectiveness of these types of apps. As such, in this paper, we present a pilot study to collect empirical evidence on the effect of a mobile-based healthcare application, designed for children, that is focused on improving motor skills. Results from the protocol, which involved eighty-five participants, show that these types of apps may result in a significant change in motor skills learning. Although the study in this paper involved adult participants, the methods proposed could be adapted by special education teachers and therapists to assess the quality of other such applications used by children in various educational and therapy settings.","PeriodicalId":183803,"journal":{"name":"Proceedings of the 1st Workshop on Mobile Medical Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121294938","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}
Deployment of medical devices on human body in unsupervised environment makes their operation safety critical. Software errors such as unbounded memory access or unreachable critical alarms can cause life threatening consequences in these medical cyber-physical systems (MCPSes), where software in medical devices monitor and control human physiology. Further, implementation of complex control strategy in inherently resource constrained medical devices require careful evaluation of runtime characteristics of the software. Such stringent requirements causes errors in manual implementation, which can be only detected by static analysis tools possibly inflicting high cost of redesigning. To avoid such inefficiencies this paper proposes an automatic code generator with assurance on safety from errors such as out-of-bound memory access, unreachable code, and race conditions. The proposed code generator was evaluated against manually written code of a software benchmark for sensors BSNBench in terms of possible optimizations using conditional X propagation. The generated code was found to be 9.3% more optimized than BSNBench code. The generated code was also tested using static analysis tool, Frama-c, and showed no errors.
{"title":"Model based code generation for medical cyber physical systems","authors":"Ayan Banerjee, S. Gupta","doi":"10.1145/2676431.2676646","DOIUrl":"https://doi.org/10.1145/2676431.2676646","url":null,"abstract":"Deployment of medical devices on human body in unsupervised environment makes their operation safety critical. Software errors such as unbounded memory access or unreachable critical alarms can cause life threatening consequences in these medical cyber-physical systems (MCPSes), where software in medical devices monitor and control human physiology. Further, implementation of complex control strategy in inherently resource constrained medical devices require careful evaluation of runtime characteristics of the software. Such stringent requirements causes errors in manual implementation, which can be only detected by static analysis tools possibly inflicting high cost of redesigning. To avoid such inefficiencies this paper proposes an automatic code generator with assurance on safety from errors such as out-of-bound memory access, unreachable code, and race conditions. The proposed code generator was evaluated against manually written code of a software benchmark for sensors BSNBench in terms of possible optimizations using conditional X propagation. The generated code was found to be 9.3% more optimized than BSNBench code. The generated code was also tested using static analysis tool, Frama-c, and showed no errors.","PeriodicalId":183803,"journal":{"name":"Proceedings of the 1st Workshop on Mobile Medical Applications","volume":"16 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134079131","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}
Wei Wang, Zhilu Chen, Baoyuan Xing, Xiaochen Huang, S. Han, E. Agu
Hearing Loss is one of the three most common chronic conditions among the elderly. In many cases, an individuals hearing is only impaired at certain (not all) frequencies. Analog hearing aids boost all sound frequencies equally including frequencies in which the individuals hearing is good, causing discomfort to the user. Digital hearing aids can amplify only the specific frequencies at which a persons hearing is impaired. In this paper, we describe the design, implementation and evaluation of a smartphone digital hearing aid app. Our digital hearing aid implementation has two parts: speech processing in the frequency domain and sound classification. We used Weighted Over-Lap Add (WOLA) filter bank to decompose microphone sounds into different frequency bands that are then amplified in the frequency domain. Mel-frequency cepstral coefficients (MFCC) of input sounds are computed and used as features for sound classification by the Gaussian Mixture Model (GMM) machine learning model. Our digital hearing aid app amplifies select frequency bands and correctly classifies speech in quiet and noisy environments. The results of a small user evaluation of our prototype are also promising.
{"title":"A smartphone-based digital hearing aid to mitigate hearing loss at specific frequencies","authors":"Wei Wang, Zhilu Chen, Baoyuan Xing, Xiaochen Huang, S. Han, E. Agu","doi":"10.1145/2676431.2676435","DOIUrl":"https://doi.org/10.1145/2676431.2676435","url":null,"abstract":"Hearing Loss is one of the three most common chronic conditions among the elderly. In many cases, an individuals hearing is only impaired at certain (not all) frequencies. Analog hearing aids boost all sound frequencies equally including frequencies in which the individuals hearing is good, causing discomfort to the user. Digital hearing aids can amplify only the specific frequencies at which a persons hearing is impaired. In this paper, we describe the design, implementation and evaluation of a smartphone digital hearing aid app. Our digital hearing aid implementation has two parts: speech processing in the frequency domain and sound classification. We used Weighted Over-Lap Add (WOLA) filter bank to decompose microphone sounds into different frequency bands that are then amplified in the frequency domain. Mel-frequency cepstral coefficients (MFCC) of input sounds are computed and used as features for sound classification by the Gaussian Mixture Model (GMM) machine learning model. Our digital hearing aid app amplifies select frequency bands and correctly classifies speech in quiet and noisy environments. The results of a small user evaluation of our prototype are also promising.","PeriodicalId":183803,"journal":{"name":"Proceedings of the 1st Workshop on Mobile Medical Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116014797","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}
{"title":"Proceedings of the 1st Workshop on Mobile Medical Applications","authors":"","doi":"10.1145/2676431","DOIUrl":"https://doi.org/10.1145/2676431","url":null,"abstract":"","PeriodicalId":183803,"journal":{"name":"Proceedings of the 1st Workshop on Mobile Medical Applications","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132031161","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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