Abstract: In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular EMA trigger. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant’s activity, conversation status, audio, and location, we can predict if an EMA triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.41. Using this knowledge, the researchers conducting field studies can efficiently schedule EMAs and achieve higher response rates.
Varun Mishra, Byron Lowens, Sarah Lord, Kelly Caine, and David Kotz. Investigating Contextual Cues As Indicators for EMA Delivery. In Proceedings of the International Workshop on Smart & Ambient Notification and Attention Management (UbiTtention), pages 935-940, September 2017. ACM. DOI 10.1145/3123024.3124571.
Taylor Hardin presented a poster at ACM MobiSys conference this week, about some clever new ideas for protecting the memory inside an MSP430 when mutually-untrusted apps have to share the same small memory. Abstract below.
Taylor Hardin explains his work to attendees at MobiSys.
We’ve just released a version 1.1 of the hardware, with many fixes and improvements; see our GitHub site.
- Changed from spring terminals to SPI-BI-WIRE POGO pin connector for programming both the MSP430 and nRF51822
- Repositioned the LCD screen to provide more room for the programmer ports and LEDs
- Broke out UART TX/RX lines for debugging the nRF51822
- Complete case redesign to better fit the mother-daughter boards, buttons, and LCD screen
- Replaced the 4 pin charging connector with a more sturdy USB charging port
David Harmon ’17 develops and evaluates a novel protocol for secure transfer of sensor data from an Amulet to a smartphone, in this Senior Honors Thesis released as a Dartmouth Computer Science Technical Report.
Abstract. The authenticity, confidentiality, and integrity of data streams from wearable healthcare devices are critical to patients, researchers, physicians, and others who depend on this data to measure the effectiveness of treatment plans and clinical trials. Many forms of mHealth data are highly sensitive; in the hands of unintended parties such data may reveal indicators of a patient’s disorder, disability, or identity. Furthermore, if a malicious party tampers with the data, it can affect the diagnosis or treatment of patients, or the results of a research study. Although existing network protocols leverage encryption for confidentiality and integrity, network-level encryption does not provide end-to-end security from the device, through the smartphone and database, to downstream data consumers. In this thesis we provide a new open protocol that provides end-to-end authentication, confidentiality, and integrity for healthcare data in such a pipeline.
We present and evaluate a prototype implementation to demonstrate this protocol’s feasibility on low-power wearable devices, and present a case for the system’s ability to meet critical security properties under a specific adversary model and trust assumptions.
Advisor: David Kotz.
George Boateng, M.S., reports on new Amulet research in his Master’s thesis, available as a Dartmouth Computer Science Technical Report.
Abstract. Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person’s daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present ActivityAware, an application on the Amulet wrist-worn device that monitors the daily activity levels (low, moderate and vigorous) of older adults in real-time. The app continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day’s accumulated time spent at that activity level, displays the results on the screen and logs summary data for later analysis.
The app implements an activity-level detection model we developed using a Linear Support Vector Machine (SVM). We trained our model using data from a user study, where subjects performed common physical activities (sit, stand, lay down, walk and run). We obtained accuracies up to 99.2% and 98.5% with 10-fold cross validation and leave-one-subject-out (LOSO) cross-validation respectively. We ran a week-long field study to evaluate the utility, usability and battery life of the ActivityAware system where 5 older adults wore the Amulet as it monitored their activity level. The utility evaluation showed that the app was somewhat useful in achieving the daily physical activity goal. The usability feedback showed that the ActivityAware system has the potential to be used by people for monitoring their activity levels. Our energy-efficiency evaluation revealed a battery life of at least 1 week before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for epidemiological studies, and eventually for the development of interventions that could improve the health of older adults.
Advisors: David Kotz, Ryan Halter, John Batsis
Congratulations to Emily Greene, an undergraduate in the Amulet group, who received Honorable Mention for the NCWIT Collegiate Award from the National Center for Women & Information Technology (NCWIT). Emily is spending this week at the NCWIT Summit on Women and IT in Tucson, Arizona.
Here is her video submission, which describes a cryptographically-supported mechanism for selective sharing of streams of mHealth data, such as those that would be produced by Amulet applications.
This presentation was captured and shared by the University of Washington when Professor David Kotz visited to present the Amulet project in a CS department colloquium, early in December 2016.
Today at the ACM Conference on Embedded Networked Sensor Systems (SenSys 2016) the Amulet team presented a paper about the design and evaluation of the Amulet platform – and unveiled a video overview of the platform and its capabilities. Check out the specs below the photo.
Indeed, we are pleased to share the Amulet hardware and software, open-source on GitHub, under a generous license that allows free use by the research community. We encourage you to download the details, fabricate your own Amulet wearable, and let us know what you think!
The Amulet team is collaborating with geriatrician Dr. John Batsis of Dartmouth-Hitchcock Medical Center to explore the use of the Amulet in developing intervension methods to help obese elderly people maintain function while they remain living at home. This “grant … from the National Institute on Aging will allow Dr. John A. Batsis to focus on strategies for improving health care delivery and wellness in older adults with obesity by using video conferencing, personal monitoring devices and frequent coaching by healthcare providers.” [press release]
Check out the video interview with Dr. Batsis on WCAX television.
This research is supported by the National Institute On Aging of the National Institutes of Health under Award Number K23AG051681.
The Amulet research group fall 2016 (L to R): David Kotz, Ron Peterson, Emily Walters, Joe Skinner, Vivian Motti, Kelly Caine, Jacob Sorber, George Boateng, Josiah Hester, Gunnar Pope, Steven Hearndon, Varun Mishra, Byron Lowens, Kevin Storer, Sarah Lord, Taylor Hardin, Ryan Halter; missing Emily Greene and Emma Oberstein.