About David Kotz

David Kotz is the Champion International Professor in the Department of Computer Science. He previously served as Interim Provost, as Associate Dean of the Faculty for the Sciences, as the Executive Director of the Institute for Security Technology Studies, and on the US Healthcare IT Policy Committee. His research interests include security and privacy, pervasive computing for healthcare, and wireless networks. He has published over 175 refereed journal and conference papers and obtained over $66m in grant funding. He is an Fellow of the IEEE, a Distinguished Member of the ACM, a 2008 Fulbright Fellow to India, and an elected member of Phi Beta Kappa. After receiving his A.B. in Computer Science and Physics from Dartmouth in 1986, he completed his Ph.D in Computer Science from Duke University in 1991 and returned to Dartmouth to join the faculty. For more information see http://www.cs.dartmouth.edu/~dfk/.

Use of Amulet in behavioral change for geriatric obesity management

John Batsis and the Amulet team just published a paper regarding Use of Amulet in behavioral change for geriatric obesity management.

Background: Obesity in older adults is a significant public health concern. Weight-loss interventions are known to improve physical function but risk the development of sarcopenia. Mobile health devices have the potential to augment existing interventions and, if designed accordingly, could improve one’s physical activity and strength in routine physical activity interventions. Methods and results: We present Amulet, a mobile health device that has the capability of engaging patients in physical activity. The purpose of this article is to discuss the development of applications that are tailored to older adults with obesity, with the intention to engage and improve their health. Conclusions: Using a team-science approach, Amulet has the potential, as an open-source mobile health device, to tailor activity interventions to older adults.

John A. Batsis, Alexandra B. Zagaria, Ryan J. Halter, George G. Boateng, Patrick Proctor, Stephen J. Bartels, and David Kotz. Use of Amulet in behavioral change for geriatric obesity management. Journal of Digital Health, 5, June 2019. DOI 10.1177/2055207619858564.

Technology for Behavioral Change in Rural Older Adults with Obesity

A new paper from the extended Amulet group.

John A. Batsis, John A. Naslund, Alexandra B. Zagaria, David Kotz, Rachel Dokko, Stephen J. Bartels & Elizabeth Carpenter-Song. Technology for Behavioral Change in Rural Older Adults with ObesityJournal of Nutrition in Gerontology and Geriatrics, April 2019.DOI: 10.1080/21551197.2019.1600097

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Sensing stress with commodity sensors

The Amulet group has been developing sensors, apps, and algorithms for sensing stress, in the field.  In one of the first papers to come out of that effort, presented today in a UbiComp workshop, we explore the potential for detecting stress using a single commodity wearable sensor.

Varun Mishra, Gunnar Pope, Sarah Lord, Stephanie Lewia, Byron Lowens, Kelly Caine, Sougata Sen, Ryan Halter, and David Kotz. The Case for a Commodity Hardware Solution for Stress Detection. In Workshop on Mental Health: Sensing & Intervention, pages 1717-1728, October 2018. ACM. DOI 10.1145/3267305.3267538.

Abstract: Timely detection of an individual’s stress level has the potential to expedite and improve stress management, thereby reducing the risk of adverse health consequences that may arise due to unawareness or mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical grade sensors strapped to the user. These sensors measure physiological signals of a person and are often bulky, custom-made, expensive, and/or in limited supply, hence limiting their large-scale adoption by researchers and the general public. In this paper, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, non-clinical sensors to capture physiological signals, and make inferences about the wearer’s stress level based on that data. In this paper, we describe a system involving a popular off-the-shelf heart-rate monitor, the Polar H7; we evaluated our system in a lab setting with three well-validated stress-inducing stimuli with 26 participants. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F1 score of 0.81, on par with clinical-grade sensors.

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Wearable Privacy: Skeletons in the Data Closet

Equipped with sensors that are capable of collecting physiological and environmental data continuously, wearable technologies have the potential to become a valuable component of personalized healthcare and health management. However, in addition to the potential benefits of wearable devices, the widespread and continuous use of wearables also poses many privacy challenges. In some instances, users may not be aware of the risks associated with wearable devices, while in other cases, users may be aware of the privacy-related risks, but may be unable to negotiate complicated privacy settings to meet their needs and preferences. This lack of awareness could have an adverse impact on users in the future, even becoming a “skeleton in the closet.” In this work, we conducted 32 semi-structured interviews to understand how users perceive privacy in wearable computing. Results suggest that user concerns toward wearable privacy have different levels of variety ranging from no concern to highly concerned. In addition, while user concerns and benefits are similar among participants in our study, these variablesshould be investigated more extensively for the development of privacy enhanced wearable technologies.

  • Byron Lowens, Vivian G. Motti, and Kelly E. Caine. Wearable Privacy: Skeletons in the Data Closet. Proceedings of IEEE International Conference on Healthcare Informatics (ICHI). Park City, UT, 2017, pp. 295-304. DOI: 10.1109/ICHI.2017.29
photo of Byron Lowens presenting his paper, "Wearable Privacy: Skeletons in the Data Closet" at ICHI 2017

Byron presenting his paper, “Wearable Privacy: Skeletons in the Data Closet” at ICHI 2017

Amulet paper at UbiTtention/Ubicomp

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.

Amulet 1.1

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

 

Cryptographic transfer of sensor data from the Amulet to a smartphone

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.