Wearable Health Monitoring
Overview
The Annual World Report on Disability reveals that 15% of the world's population lives with a disability, while 110 to 190 million people face significant difficulties in functioning. Diagnosis, treatment, and rehabilitation currently depend on the behavior observed in a clinical environment. After the patient leaves the clinic, there is no standard approach to continuously monitor the patient and report potential problems [Espay et al.]. Furthermore, self-recording is inconvenient and unreliable. The quality of life of this population can be improved significantly with the help of wearable internet-of-things (IoT) devices that combine sensing, processing and wireless communication capabilities within a small form-factor.
Wearable sensors and mobile health applications are emerging as attractive solutions to augment clinical treatment and enable telepathic diagnostics [Espay et al.]. Wearable technology allows continuous monitoring of user movement in a free-living home environment. This helps in capturing the progression of symptoms that change over time. Furthermore, it enables evaluating the prescribed therapy on an individual basis. Recently, wearable sensors and smartphones have shown promising results in the diagnosis of essential tremor [Daneault] and detecting seizures in epilepsy [Ozanne et al.]. Despite recent promising results, widespread adoption of wearable devices for health monitoring has been hindered by three major challenges. First, small form-factor IoT devices must operate under extreme energy constraints, since large batteries are prohibitive. Second, conventional rigid devices are uncomfortable and awkward to wear for long periods of time. Therefore, current wearable devices are largely limited to watches and wristbands. Finally, the value of wearable IoT devices must be demonstrated by high-impact applications to expedite their adoption.
To address these three challenges, we perform research in the following areas:
Energy-neutral operation through optimal energy harvesting and management [Bhat et al. (2017)],
Wearable IoT devices using Flexible Hybrid Electronics (FHE) [Bhat et al. (2016), Bhat et al. (2019a)],
Development of an online learning framework for human activity recognition(HAR) [Bhat et al. (2018), Park et al. (2017), Bhat et al. (2019b)].
Application Domains
A variety of wearable applications ranging from fitness tracking to continuous health monitoring of patients with movement disorders will be able to exploit the OpenHealth platform.
The Reference applications included with the OpenHealth release.
Human Activity Recognition
Data sets for human activity recognition are available at our GitHub page.
Gesture Recognition
The hardware stack in the wearable platform mainly consists of some common sensors, a Microcontroller, radio and an energy harvesting circuitry.
The base firmware stack consists of the real time operating system (RTOS), sensor APIs, communication services as well as reference applications.
This research is performed at the eLab research group at ASU.
Relevant Papers:
Hang Gao, Ganapati M. Bhat, Umit Y. Ogras, Sule Ozev, "Optimized Stress Testing for Flexible Hybrid Electronics Designs," IEEE VLSI Test Symposium, April 2019.
Md Muztoba, Rohit Voleti, Fatih Karabacak, Jaehyun Park, and Umit Y. Ogras, “Instinctive Assistive Indoor Navigation using Distributed Intelligence,” ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 23, issue 6, December 2018.
Fatih Karabacak, Umit Y. Ogras and Sule Ozev, “Remote Detection of Unauthorized Activity via Spectral Analysis,” ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 23, issue 6, December 2018.
Doohwang Chang, Ganapati Bhat, Umit Y. Ogras, Bertan Bakkaloglu, and Sule Ozev, “Detection Mechanisms for Unauthorized Wireless Transmissions,” ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 23, issue 6, December 2018.
Ganapati Bhat, Ranadeep Deb, Vatika Vardhan Chaurasia, Holly Shill, and Umit Y. Ogras. "Online Human Activity Recognition using Low-Power Wearable Devices", in Proc. of Intl. Conf. on Computer-Aided Design (ICCAD), Nov. 2018. [pdf]
Fatih Karabacak,Umit Y. Ogras and Sule Ozev, “Malicious Activity Analysis for Lightweight IoT Devices,” in Proc. of GOMACTech, March 2018.
Ganapati Bhat, Jaehyun Park, and Umit Y. Ogras. "Near Optimal Energy Allocation for Self-Powered Wearable Systems,” in Proc. of Intl. Conf. on Computer-Aided Design (ICCAD), November 2017. [link]
Jaehyun Park, Hitesh Joshi, Hyung Gyu Lee, Sayfe Kiaei, and Umit Y. Ogras. “Flexible PV-cell Modeling for Energy Harvesting in Wearable IoT Applications,” in ACM Tran. on Embedded Comp. Sys. (ESWEEK Special Issue), October 2017. (CODES+ISSS: Best Paper Award) [link][poster]
Ujjwal Gupta, Jaehyun Park, Hitesh Joshi, Umit Y. Ogras. "Flexibility Aware Systems on Polymer: Concept to Prototype," in IEEE Trans. on Multi-Scale Computing Systems, December 2016.
Cemil Geyik, Arindam Dutta, Umit Y. Ogras, Daniel W. Bliss, Decoding Human Intent using a Wearable System and Multi-Modal Sensor Data, in Proc. of the Asilomar Conf. on Signals, Systems, and Computers, November 2016.
Ganapati Bhat, Ujjwal Gupta, Nicholas Tran*, Jaehyun Park, Sule Ozev, Umit Y. Ogras. "Multi-Objective Design Optimization for Flexible Hybrid Electronics," in Proc. of Intl. Conf. on Computer-Aided Design (ICCAD), November 2016 (*undergraduate student). [link]
Alexandra Porter*, Md Muztoba, Umit Y. Ogras, “Human-Machine Communication for Assistive IoT Technologies,” (Extended Abstract) IoT Day at the Embedded Systems Week, October 2016 (*undergraduate student).
Ujjwal Gupta and Umit Y. Ogras. "Extending Networks from Chips to Flexible and Stretchable Electronics," in Proc. of Intl. Symp. on Networks-on-chip, August 2016.
Md Muztoba, Ujjwal Gupta, Tanvir Mustofa, Umit Y. Ogras, “Robust Communication with IoT Devices using Wearable Brain Machine Interfaces,” in Proc. of Intl. Conference on Computer-Aided Design (ICCAD), November 2015.
Md Muztoba, Eric Qin*, Nicholas Tran* and Umit Y. Ogras, “Context-aware Control of Smart Objects via Human-Machine Communication,” in Proc. of Biomedical Circuits & Systems (BioCAS) Conference, October 2015. (*undergraduate students)
Ujjwal Gupta, Sankalp Jain, Umit Y. Ogras, “Can Systems Extended to Polymer? SoP Architecture Design and Challenges,” in Proc. of the Intl. System-on-Chip Conference (SOCC), September 2015. (Best paper candidate)
Thesis:
Ranadeep Deb. How Does Technology Development Influence the Assessment of Parkinson’s Disease? A Systematic Review. Master’s thesis, School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, 2019. [Thesis]