Pritika Dasgupta1, James Alexander Hughes2, Mark Daley3, Ervin Sejdić4. 1. Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA. Electronic address: prd17@pitt.edu. 2. Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, B2G 2W5, Canada. 3. Department of Computer Science, Middlesex College, University of Western Ontario, London, Ontario, N6A 3K7, Canada. 4. Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA; Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA; Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
Abstract
BACKGROUND AND OBJECTIVE: Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes. METHODS: While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion. RESULTS: With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes. CONCLUSIONS: A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.
BACKGROUND AND OBJECTIVE: Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes. METHODS: While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion. RESULTS: With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes. CONCLUSIONS: A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.
Authors: Michael D Schmidt; Ravishankar R Vallabhajosyula; Jerry W Jenkins; Jonathan E Hood; Abhishek S Soni; John P Wikswo; Hod Lipson Journal: Phys Biol Date: 2011-08-10 Impact factor: 2.583
Authors: Malaak Nasser Moussa; Crystal D Vechlekar; Jonathan H Burdette; Matt R Steen; Christina E Hugenschmidt; Paul J Laurienti Journal: Front Hum Neurosci Date: 2011-08-22 Impact factor: 3.169