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Wearable Health Monitoring

Wearable health technology is drawing significant attention for good reasons. The pervasive nature of such systems providing ubiquitous access to information will transform the way people interact with each other and their environment. The resulting information extracted from these systems will enable emerging applications in healthcare, wellness, emergency response, fitness monitoring, elderly care support, long-term preventive chronic care, assistive care, smart environments, sports, gaming, and entertainment which create many new research opportunities and transform researches from various disciplines.

Despite the ground-breaking potentials, there are a number of interesting challenges in order to design and develop wearable medical embedded systems. Due to limited available resources in wearable processing architectures, power-efficiency is demanded to allow unobtrusive and long-term operation of the hardware. Also, the data-intensive nature of continuous health monitoring requires efficient signal processing and data analytics algorithms for real-time, scalable, reliable, accurate, and secure extraction of relevant information from an overwhelmingly large amount of data. Therefore, extensive research in their design, development, and assessment is necessary.

 

Embedded Processing Platform Design

The majority of my work concentrates on designing wearable embedded processing platforms in order to shift the conventional paradigms from hospital-centric healthcare with episodic and reactive focus on diseases to patient-centric and home-based healthcare as an alternative segment which demands outstanding specialized design in terms of hardware design, software development, signal processing and uncertainty reduction, data analysis, predictive modeling and information extraction. The objective is to reduce the costs and improve the effectiveness of healthcare by proactive early monitoring, diagnosis, and treatment of diseases (i.e. preventive).

 

Big Data

There are several challenges in the development, validation, and utilization of complex sensing and monitoring systems to produce reliable and consistent measurements in uncontrolled user environments. Massive data is generated with different nature that needs to be analyzed in spatial, spectral, and temporal space individually and in groups. We use big data platforms and clusters to analyze the ultra-high dimensional space problems and extract more efficient features.

 

News

[December 2017] Congrats to Muhamed on his paper’s acceptance:

Dehzangi, Omid, Farooq, Muhamed, “Portable Brain Computer Interface for the Intensive Care Unit Patient Communication using Subject-Dependent SSVEP Identification”, Portable and Wearable Brain Technologies for Neuroenhancement and Neurorehabilitation, BioMed International Research (2017).

[November 2017] Congrats to Mojtaba on his paper’s acceptance:

Dehzangi, Omid, Taheri, Mojtaba, ChangalVala, Raghvendar, “IMU-based Gait Recognition using Convolutional Neural Networks and Multi-Sensor Fusion”,  Sensors 2017, 17(12), 2735; doi:10.3390/s17122735 (registering DOI).

[November 2017] WSSP Thanksgiving dinner:

 

[October 2017] Congrats to Omar, Bahvani, and Muhamed on their papers acceptance:

  1. Dehzangi, Omid, Iftikhar, Omar, A. Bache, Bhavani, Wensman, Jeffrey, Li, Ying “Force and Activity Monitoring System for Scoliosis Patients Wearing Back Braces”, Accepted for presentation at the 36th IEEE International Conference on Consumer Electronics (ICCE’17), January 12-14, 2018, Las Vegas.
  2. Dehzangi, Omid, Farooq, Muhamed “Wearable Brain Computer Interface (BCI) to Assist Communication in the Intensive Care Unit (ICU)”, Accepted for presentation at the 36th IEEE International Conference on Consumer Electronics (ICCE’17), January 12-14, 2018, Las Vegas.

[October 2017] Congrats to Muhamed, Omar, and  Bahvani on their papers acceptance:

  1. Dehzangi, Omid, Farooq, Muhamed “Subject-Dependent SSVEP Identification Using GMM Training and Adaptation”, Accepted for presentation at the 16th IEEE International Conference On Machine Learning And Applications, DECEMBER 18-21, 2017 | CANCUN, MEXICO.
  2. Dehzangi, Omid, A. Bache, Bhavani, Iftikhar, Omar, Wensman, Jeffrey, Li, Ying “Context-Aware Remote Monitoring of Brace treatment Compliance for Adolescent Idiopathic Scoliosis Patients”, Accepted for presentation at the 16th IEEE International Conference On Machine Learning And Applications, DECEMBER 18-21, 2017 | CANCUN, MEXICO.

[September 2017] Congrats to Alex, Mojtaba, Raghavendar, Priyanka, Shantanu, Bahvani, and Omar on their papers acceptance. Congrats to Raghavendar, Priyanka on the best paper award:

  1. Dehzangi, Omid, Melville, Alex, Taherisadr, Mojtaba “Automatic EEG blink Detection using Dynamic TimeWarping Score Clustering”, the 12th International Conference on Body Area Networks SEPTEMBER 28–29, 2017 DALIAN, PEOPLE’S REPUBLIC OF CHINA.
  2. Dehzangi, Omid, Taherisadr, Mojtaba “EEG Based Driver Inattention Identification via Feature Profiling and Dimensionalty Reduction”, the 12th International Conference on Body Area Networks SEPTEMBER 28–29, 2017 DALIAN, PEOPLE’S REPUBLIC OF CHINA.
  3. Dehzangi, Omid, Taherisadr, Mojtaba”EEG-Based Driver Distraction Detection via Cooperative-Game-Theoretic Channel Characterization and Selection*”, the 12th International Conference on Body Area Networks SEPTEMBER 28–29, 2017 DALIAN, PEOPLE’S REPUBLIC OF CHINA.
  4. Dehzangi, Omid, Changalvala, Raghavendar, Taherisadr, Mojtaba, Asnani, Priyanka “Motion-Based Gait Identification Using Spectro-Temporal Transform and Convolutional Neural Networks”, the the 12th International Conference on Body Area Networks SEPTEMBER 28–29, 2017 DALIAN, PEOPLE’S REPUBLIC OF CHINA (best paper award winner).
  5. Deshmukh, Shantanu, Dehzangi, Omid “Characterization and Identification of Driver Distraction During Naturalistic Driving: An Analysis of ECG Dynamics”, the 12th International Conference on Body Area Networks SEPTEMBER 28–29, 2017 DALIAN, PEOPLE’S REPUBLIC OF CHINA.
  6. Rajendra, Vika, Dehzangi, Omid “Wearable Galvanic Skin Response for real-time Characterization and Identification of Distraction During Naturalistic”, the 12th International Conference on Body Area Networks SEPTEMBER 28–29, 2017 DALIAN, PEOPLE’S REPUBLIC OF CHINA.
  7. Dehzangi, Omid, Bache Bhavani , Iftikhar, Omar, Wensman, Jeffrey , Li, Ying “Context-Aware Sensor Solution for Remote Monitoring of Adolescent Idiopathic Scoliosis Brace Treatment”, the 12th International Conference on Body Area Networks SEPTEMBER 28–29, 2017 DALIAN, PEOPLE’S REPUBLIC OF CHINA.

[May 2017] Congrats to Shantanu on his paper acceptance:

Shantanu Deshmukh, Omid Dehzangi, “ECG-based Driver Distraction Identification using Discriminative Kernel-based Features”, IEEE International Conference on Smart Computing (SMARTCOMP’17), MAY 29-31, 2017

[May 2017] WSSP lab was among the MTRACK semi-Finalists for the effort on driver monitoring system

[May 2017] Congrats to Vikas, Shantanu, and Muhamed on their papers acceptance:

  1. Vikas Rajendra, Omid Dehzangi, “Detection of Distraction under Naturalistic Driving Using Galvanic Skin Responses”, the 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN2017), May 9-12, 2017.
  2. Shantanu Deshmukh, Omid Dehzangi, “Real-time Identification of Driver Distraction using Wavelet Packet Transform Features”, the 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN2017), May 9-12, 2017.
  3. Muhamed Farooq, Omid Dehzangi, “High Accuracy Wearable SSVEP Detection using Feature Profiling and Dimensionality Reduction”, the 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN2017), May 9-12, 2017.

[April 2017] Congrats to WSSP lab for the approval of the Ford Project for $220,000 on Seamless Physiological/Behavioral Monitoring on Multi-environments.