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A Comprehensive Survey 

Human Activity Recognition Research Paper [link]

Nowadays, the aging population is becoming one of the world’s primary concerns. It is estimated that the population aged over 65 will increase from 461 million to 2 billion by 2050. Such a substantial increase in the elderly population will have significant social and health care consequences. To monitor older adults’ physical, functional, and cognitive health in their homes, Human Activity Recognition (HAR) is emerging as a powerful tool.  

Workflow showing daily environment sensors, preprocessing steps, pattern recognition techniques, and evaluation.

HAR’s goal is to recognize human activities of daily life (for example, walking, standing, sleeping, running, repose, watching TV, cooking, etc.) in controlled and uncontrolled settings.  

i-Heal’s lab director, Dr. Parisa Rashidi, teamed up with Dr. Florenc Demrozi and Dr. Graziano Pravadelli from University of Verona, Italy and Dr. Azra Bihorac from UF Prisma-P to review the latest advancements in HAR. 

This paper provides an in-depth systematic review of the state-of-the-art in HAR approaches, published from January 2015 to September 2019, based on Classic Machine Learning and Deep Learning, which make use of data collected by sensors (inertial or physiological), embedded into wearables or environment. 

Overview of the proposed survey structure on sensor-based
HAR research results from 2015 to 2019.

We surveyed methodologies based on sensor type, device type (smartphone, smartwatch, or standalone), preprocessing step (noise removal or feature extraction technique), and finally, their Deep Learning or Classic Machine Learning model. The results are presented in terms of a) average activity recognition accuracy, b) the average number of studied activities, and c) the average number of datasets used to test the methodology. 

We presented results both from the point of view of quality (accuracy) and quantity (number of recognized activities). We concluded that HAR researchers still prefer Classic Machine Learning models, mainly because they require a smaller amount of data and less computational power than Deep Learning models. However, the Deep Learning models have shown higher capacity in recognizing many complex activities.  

Future work in Human Activity Recognition applications should focus on developing more advanced generalization capabilities and recognition of more complex activities. 

Read the full research paper here.

  Posts

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April 7th, 2021

Dr. Rashidi at ISN Virtual World Congress of Nephrology 2021

Dr. Rashidi will join Dr. Azra Bihorac and Dr. Yoshua Bengio in a discussion titled “How to achieve equitable, inclusive, and ethical AI development and implementation” at ISN Virtual World Congress of Nephrology 2021.

February 4th, 2021

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors

A Comprehensive Survey  Human Activity Recognition Research Paper [link] Nowadays, the aging population is becoming one of the world’s primary […]

June 6th, 2019

NIH Mitchel Max Award- Finalist

Dr. Rashidi is nominated as one of the three finalists for the National Institute of Health (NIH) Mitchel Max Award […]

May 3rd, 2019

HWCOE Excellence Award

Original Article: Link Parisa Rashidi, Ph.D., areceived the HWCOE Excellence Award for Assistant Professors. This award is given to faculty […]

May 3rd, 2019

Provost Excellence Award

Main Article: Link Parisa Rashidi, Ph.D., an assistant professor in the J. Crayton Pruitt Family Department of Biomedical Engineering, has […]

February 25th, 2019

News Coverage in CBS

A first of its kind technology developed here in Gainesville can predict the probability and possible cause of death in […]

February 25th, 2019

News Coverage in Fox13

Artificial intelligence used in the ICU to predict mortality, news story: Watch the video here: link

February 22nd, 2019

News Coverage in Alligator Newspaper

Excerpt from the original story:   UF researchers can now assess and treat a patient’s condition faster than ever before […]

February 19th, 2019

News Coverage in UFHealth News

n a hospital’s intensive care unit, doctors get a cascade of data about each patient’s condition that can be challenging […]

February 18th, 2019

NIH Trailbalzer Award

The under-assessment of pain response is one of the primary barriers to the adequate treatment of pain in critically ill […]