Farrokh F. Mohammadzadeh

Works (3)

Updated: July 5th, 2023 15:37

2019 journal article

Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses

SENSORS, 19(3).

By: N. Starliper n, F. Mohammadzadeh n, T. Songkakul n , M. Hernandez *, A. Bozkurt n & E. Lobaton n 

author keywords: wearable health; physiological prediction; activity clustering; multi-modal data; Body Sensor Networks; sensor selection; power efficient sensing
MeSH headings : Accelerometry / statistics & numerical data; Actigraphy / instrumentation; Cluster Analysis; Electric Power Supplies / economics; Humans; Telemedicine / economics; Wearable Electronic Devices / economics; Wearable Electronic Devices / standards
TL;DR: This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams, and finds the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. (via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: March 18, 2019

2017 conference paper

Energy-efficient activity recognition via multiple time-scale analysis

2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1466–1472.

By: N. Lokare n, S. Samadi n, B. Zhong n , L. Gonzalez n, F. Mohammadzadeh n & E. Lobaton n 

TL;DR: This work proposes a novel power-efficient strategy for supervised human activity recognition using a multiple time-scale approach, which takes into account various window sizes, and shows that the proposed approach Sequential Maximum-Likelihood achieves high F1 score across all activities while providing lower power consumption than the standard Maximum- likelihood approach. (via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Sources: NC State University Libraries, NC State University Libraries, ORCID
Added: August 6, 2018

2015 journal article

Feasibility of a Wearable, Sensor-based Motion Tracking System

Procedia Manufacturing, 3, 192–199.

By: F. Mohammadzadeh n, S. Liu n, K. Bond n & C. Nam n 

author keywords: Healthcare; Motion tracking; Wearable wireless sensor
TL;DR: The results suggest that an IMU-based (more specifically, a gyroscope-based) motion tracking system can be realistically used to accurately track a patient's motion without the need of numerous sensors or an overly complicated set-up. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, Crossref, NC State University Libraries
Added: August 6, 2018

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