A comparison of activity classification in younger and older cohorts using a smartphone

Abstract

Automatic recognition of human activity is useful as a means of estimating energy expenditure and has potential for use in fall detection and prediction. The emergence of the smartphone as a ubiquitous device presents an opportunity to utilize its embedded sensors, computational power and data connectivity as a platform for continuous health monitoring. In the study described herein, 37 older people (83.9 ± 3.4 years) performed a series of activities of daily living (ADLs) while a smartphone (containing a triaxial accelerometer, triaxial gyroscope and barometric pressure sensor) was placed in the front pocket of their trousers. These results are compared to a similar trial conducted previously in which 20 young people (21.9 ± 1.65 years) were asked to perform the same ADLs using the same smartphone (again in the front pocket of their trousers).In each trial, the participants were asked to perform several activities (standing, sitting, lying, walking on level ground, up and down staircases, and riding an elevator up and down) in a free-living environment. During each acquisition session, the internal sensor signals were recorded and subsequently used to develop activity classifiers based on a decision tree algorithm that classified ADL in epochs of ~1.25 s. When training and testing with the younger cohort, using a leave-one-out cross validation procedure, a total classification sensitivity of 80.9% ± 9.57% ([Formula: see text] = 0.75 ± 0.12) was obtained. Retraining and testing on the older cohort, again using cross validation, gives a comparable total class sensitivity of 82.0% ± 8.88% ([Formula: see text] =0.74 ± 0.12).When trained with the younger group and tested on the older group, a total class sensitivity of 69.2% ± 24.8% (95% confidence interval [69.6%, 70.6%]) and [Formula: see text] = 0.60 ± 0.27 (95% confidence interval [0.58, 0.59]) was obtained. When trained on the older group and tested on the younger group, a total class sensitivity of 80.5% ± 6.80% (95% confidence interval [79.0%, 80.6%]) and [Formula: see text] = 0.74 ± 0.08 (95% confidence interval [0.73, 0.75]) was obtained.An instance of the decision tree classifier developed was implemented on the smartphone as a software application. It was capable of performing real-time activity classification for a period of 17 h on a single battery charge, illustrating that smartphone technology provides a viable platform on which to perform long-term activity monitoring.

Wearables for independent living in older adults: Gait and falls

“…100 data points/second). This negatively impacts battery life and free-living efficiency of wearables for gait and fall assessment [113]. Consequently, the demand for energy optimisation techniques [114] and new ways to configure wearable software functionality [115] are ongoing engineering challenges.…”

Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle

“…With a total of 50 h of acquired data, they obtained 88.3% classification accuracy using the accelerometer alone and 98.4% using both the accelerometer and barometric pressure sensors. Del Rosario et al (10) performed activity classification using a smartphone embedded accelerometer, gyroscope and barometric pressure sensor, evaluating performance on 20 young adults (21.9 ± 1.7 yo) and 37 older adults persons (83.9 ± 3.4 yo). The same feature set was used in both age groups to classify 9 activities (stand, sit, lie, walk, walk upstairs, walk downstairs) from 10-30 minutes of data per person collected when the smartphone was kept in a person's trousers front pocket.…”

Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study

“…A typical IMU device is composed of a tri-axial accelerometer and gyroscope capable of measuring linear acceleration and angular velocity. There is an increasing number of physical activity classification (PAC) systems to classify the ADL by utilizing these sensors [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]. The overall performance of these PAC systems presented in the literature can depend on many factors, illustrated in Figure 1.…”

“…The window sizes largely differs across the PAC systems proposed in the literature: 2 s [4], 2.5 s [11], 5 s [5], 5.12 s [3], 6.7 s [2], and 10 s [9]. The overlapping interval used in most of the PAC systems is 50% of the window size [20].…”

“…Due to the large diversity in the design process, the existing PAC systems are not directly comparable which hinders the development of new techniques informed by the strengths and the gaps in these systems. Another issue is that most of the existing PAC systems used younger subjects for data collection [3,4,5,6,9,10,13,14,17,21,22] and few systems collected data on older subjects [11,23,24,25,26]. Furthermore, most PAC systems are developed in a controlled environment, which is quite different from real-life conditions [27].…”