Automated Analysis and Quantification of Human Mobility Using a Depth Sensor

Leightley, Daniel; McPhee, Jamie S.; Yap, Moi Hoon

doi:10.1109/jbhi.2016.2558540

Cited by 43 publications

(32 citation statements)

References 35 publications

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“…In the current study, we demonstrate a much simpler method of calculating CoM, first used by Leightley et al [28], is able to achieve similar results. Leightley's method takes the euclidean mean of 3, well-tracked joints (hip left, hip right, spine mid) to be a good estimate of the CoM position.…”

Section: Introductionsupporting

confidence: 72%

“…Frame-wise calculation of CoM The position of CoM was calculated, frame-by-frame by taking the euclidean average of the left-hip, right-hip and mid-spine joints, as defined by Eq 2, first used by Leightley et al [28]. This method estimates the position of CoM in three dimensions without needing to rely on the assumptions made by the Balance Master.…”

Section: Recording Of Com Pathmentioning

confidence: 99%

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A new process to measure postural sway using a Kinect depth camera during a Sensory Organisation Test

et al. 2020

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Posturography provides quantitative, objective measurements of human balance and postural control for research and clinical use. However, it usually requires access to specialist equipment to measure ground reaction forces, which are not widely available in practice, due to their size or cost. In this study, we propose an alternative approach to posturography. It uses the skeletal output of an inexpensive Kinect depth camera to localise the Centre of Mass (CoM) of an upright individual. We demonstrate a pipeline which is able to measure postural sway directly from CoM trajectories, obtained from tracking the relative position of three key joints. In addition, we present the results of a pilot study that compares this method of measuring postural sway to the output of a NeuroCom SMART Balance Master. 15 healthy individuals (age: 42.3 ± 20.4 yrs, height: 172 ± 11 cm, weight: 75.1 ± 14.2 kg, male = 11), completed 25 Sensory Organisation Test (SOT) on a NeuroCom SMART Balance Master. Simultaneously, the sessions were recorded using custom software developed for this study (CoM path recorder). Postural sway was calculated from the output of both methods and the level of agreement determined, using Bland-Altman plots. Good agreement was found for eyes open tasks with a firm support, the agreement decreased as the SOT tasks became more challenging. The reasons for this discrepancy may lie in the different approaches that each method takes to calculate CoM. This discrepancy warrants further study with a larger cohort, including fall-prone individuals, cross-referenced with a marker-based system. However, this pilot study lays the foundation for the development of a portable device, which could be used to assess postural control, more cost-effectively than existing equipment.

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Section: Introductionsupporting

confidence: 72%

Section: Recording Of Com Pathmentioning

confidence: 99%

A new process to measure postural sway using a Kinect depth camera during a Sensory Organisation Test

et al. 2020

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“…In recent years, wearable sensors have been widely used in gait speed estimation [28] and the assessment of a user's health state, while being based on gait abnormalities [29] and fall detection [30]. Some studies recognize and evaluate the daily activities through Microsoft Kinect sensors which measured concurrently with a 3D Mo-Cap system (gold standard), with the result showing that the Microsoft Kinect sensors can effectively identify the characteristics of patients' daily activities and, hence, that it can be used as an effective clinical evaluation method [31,32]. Wearable devices are also widely used to track human walking activities [33].…”

Section: Introductionmentioning

confidence: 99%

A Determination Method for Gait Event Based on Acceleration Sensors

Mei

Gao

Liu

2019

Sensors

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A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.

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“…After normalisation, a set of features, shown in Table II, was calculated. Although the Kinect camera provides coordinates for 25 joints, we found that features derived from just the torso joints, as defined in [5], plus a calculation of CoM were enough to discriminate the differences in motions of young and older people. Euclidean Distance was calculated between the spine base and head, using equation 2.…”

Section: Feature Encodingmentioning

confidence: 92%

A comparative study of the clinical use of motion analysis from Kinect skeleton data

Maudsley-Barton

McPhee

Bukowski

et al. 2017

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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The analysis of human motion as a clinical tool can bring many benefits such as the early detection of disease and the monitoring of recovery, so in turn helping people to lead independent lives. However, it is currently under used. Developments in depth cameras, such as Kinect, have opened up the use of motion analysis in settings such as GP surgeries, care homes and private homes. To provide an insight into the use of Kinect in the healthcare domain, we present a review of the current state of the art. We then propose a method that can represent human motions from time-series data of arbitrary length, as a single vector. Finally, we demonstrate the utility of this method by extracting a set of clinically significant features and using them to detect the age related changes in the motions of a set of 54 individuals, with a high degree of certainty (F1score between 0.9 -1.0). Indicating its potential application in the detection of a range of age-related motion impairments.

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Automated Analysis and Quantification of Human Mobility Using a Depth Sensor

Cited by 43 publications

References 35 publications

A new process to measure postural sway using a Kinect depth camera during a Sensory Organisation Test

A new process to measure postural sway using a Kinect depth camera during a Sensory Organisation Test

A Determination Method for Gait Event Based on Acceleration Sensors

A comparative study of the clinical use of motion analysis from Kinect skeleton data

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