Posture and Physical Activity Detection: Impact of Number of Sensors and Feature Type

Abstract

Studies using wearable sensors to measure posture, physical activity (PA), and sedentary behavior typically use a single sensor worn on the ankle, thigh, wrist, or hip. Although the use of single sensors may be convenient, using multiple sensors is becoming more practical as sensors miniaturize. Purpose We evaluated the effect of single-site versus multisite motion sensing at seven body locations (both ankles, wrists, hips, and dominant thigh) on the detection of physical behavior recognition using a machine learning algorithm. We also explored the effect of using orientation versus orientation-invariant features on performance. Methods Performance (F1 score) of PA and posture recognition was evaluated using leave-one-subject-out cross-validation on a 42-participant data set containing 22 physical activities with three postures (lying, sitting, and upright). Results Posture and PA recognition models using two sensors had higher F1 scores (posture, 0.89 ± 0.06; PA, 0.53 ± 0.08) than did models using a single sensor (posture, 0.78 ± 0.11; PA, 0.43 ± 0.03). Models using two nonwrist sensors for posture recognition (F1 score, 0.93 ± 0.03) outperformed two-sensor models including one or two wrist sensors (F1 score, 0.85 ± 0.06). However, two-sensor models for PA recognition with at least one wrist sensor (F1 score, 0.60 ± 0.05) outperformed other two-sensor models (F1 score, 0.47 ± 0.02). Both posture and PA recognition F1 scores improved with more sensors (up to seven; 0.99 for posture and 0.70 for PA), but with diminishing performance returns. Models performed best when including orientation-based features. Conclusions Researchers measuring posture should consider multisite sensing using at least two nonwrist sensors, and researchers measuring PA should consider multisite sensing using at least one wrist sensor and one nonwrist sensor. Including orientation-based features improved both posture and PA recognition.

[Retracted] Analysis and Risk Assessment of Corporate Financial Leverage Using Mobile Payment in the Era of Digital Technology in a Complex Environment

“…For a binary classification task, Figure 3 shows a framework diagram of the confusion matrix. TP refers to true example, FP refers to false positive example, TN refers to true negative example, and FN refers to false negative example; F1-score represents the harmonic average of precision and recall [17]. It is one of the main indexes for model evaluation with specificity.…”

Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models

“…When an accelerometer is placed on a body location that moves into a fixed anatomical plane in different postures, the changes in accelerometer orientation from tilt angle can be used to detect the posture with a high accuracy. Skotte et al [44] and Edwardson et al [45] have previously demonstrated that thigh-worn classifiers can differentiate sitting and standing, whereas Gjoreski et al [46], Tang et al [47], and Narayanan et al [48] have shown that a two-placement combination of the thigh and hip/back results in an excellent recognition of lying, sitting, and standing. Prior studies have also demonstrated that thigh-worn monitors, such as activPAL and Uptimer, provide acceptable estimates of sitting and standing among children with CP who have mild motor impairments [49,50].…”

Companion: A Pilot Randomized Clinical Trial to Test an Integrated Two-Way Communication and Near-Real-Time Sensing System for Detecting and Modifying Daily Inactivity among Adults >60 Years—Design and Protocol

“…These devices will be worn on the non-dominant wrist. Wrist sensor data will be processed using Monitor-Independent Movement Summary unit (MIMS) to yield overall movement volume estimations [ 40 ] and machine learning algorithms to yield sensor wear, sleep, and wake behavior characterization (i.e., sedentary, ambulation, and upright behaviors) [ 41 , 42 , 43 , 44 ] during the 16-week intervention period. (ii) Health outcomes : (a) anthropometrics (height, weight, and waist/hip circumference); (b) body composition (via bioelectrical impedance) [ 45 ]; (c) seated resting heart rate and blood pressure; (d) fasting blood biomarkers (blood glucose and lipid profile); (e) cognition (via The NIH Toolbox Cognition Battery) [ 46 ]; (f) physical function (via The Short Physical Performance Battery) [ 47 , 48 ]; (g) upper and lower body muscle endurance (via maximal push-up and squat tests, respectively) [ 49 ]; (h) aerobic fitness (via the six-minute walk test) [ 50 , 51 ]; and (i) other questionnaire-based health outcomes (see supplementary study protocol ).…”