Monitoring human health behaviour in one's living environment: A technological review

Lowe, Shane A.; ÓLaighin, Gearóid

doi:10.1016/j.medengphy.2013.11.010

Cited by 121 publications

(76 citation statements)

References 89 publications

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“…Each accelerometer and gyroscope data stream (x, y and z) exhibit signal patterns that are distinctive for each of the arm movements, which is characterized by a set of features extracted from the signals [15]. In this investigation, we consider 10 time-domain features, extracted from the data from each of the three accelerometer axes and from each of the three gyroscope axes as follows: 1) standard deviation -measure of the variability from the mean of the signal, 2) root mean square (rms) -measure of the signal energy normalized by the number of samples, 3) information entropy -measure of the randomness of a signal [35], 4) jerk metric -rms value of the second derivative of the data normalized with respect to the maximum value of the first derivative [36], 5) peak number -obtained from gradient analysis of the signal, 6) maximum peak amplitude -measure of the amplitude of the peaks obtained after gradient analysis, 7) absolute difference -absolute difference between the maximum and the minimum value of a signal, 8) index of dispersion -ratio of variance to the mean, 9) kurtosis -measure of the 'peakedness' of a signal assuming a non-Gaussian distribution in the data, 10) skewness -measure of the symmetry of the data assuming a non-Gaussian distribution in the data [37].…”

Section: Feature Extractionmentioning

confidence: 99%

Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification

Biswas

Cranny

Gupta

et al. 2015

Human Movement Science

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In this paper we present a methodology for recognizing three fundamental movements of the human forearm (extension, flexion and rotation) using pattern recognition applied to the data from a single wrist-worn, inertial sensor.We propose that this technique could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies such as stroke or cerebral palsy by tracking the number of times a patient performs specific arm movements (e.g. prescribed exercises) with their paretic arm throughout the day. We demonstrate this with healthy subjects and stroke patients in a simple proof of concept study in which these arm movements are detected during an archetypal activity of daily-living (ADL) -'making-a-cup-of-tea'. Data is collected from a tri-axial accelerometer and a tri-axial gyroscope located proximal to the wrist. In a training phase, movements are initially performed in a controlled environment which are represented by a ranked set of 30 time-domain features. Using a sequential forward selection technique, for each set of feature combinations three clusters are formed using k-means clustering followed by 10 runs of 10-fold cross validation on the training data to determine the best feature combinations. For the testing phase, movements performed during the ADL are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprised of the best ranked features, using Euclidean or Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and four stroke survivors and our results show that the proposed methodology can detect the three movements performed during the ADL with an overall average accuracy of 88% using the accelerometer data and 83% using the gyroscope data across all healthy subjects and arm movement types. The average accuracy across all stroke survivors was 70% using accelerometer data and 66% using gyroscope data. We also use a Linear Discriminant Analysis (LDA) classifier and a Support Vector Machine (SVM) classifier in association with the same set of features to detect the three arm movements and compare the results to demonstrate the effectiveness of our proposed methodology.

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Section: Feature Extractionmentioning

confidence: 99%

Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification

Biswas

Cranny

Gupta

et al. 2015

Human Movement Science

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“…In recent years, the increasing availability of novel wearable health technology [17][18][19][20][21][22][23] and in particular the explosion of fitness trackers (e.g. Fitbit Flex, Nike+ FuelBand, Jawbone UP band, Garmin vίvofit, Misfit shine, and flash) has made actigraphy available to the general population and it is now considered normal to track one's daily activity and sleep and receive feedback about one's overall health.…”

Section: Introductionmentioning

confidence: 99%

Validation of Sleep-Tracking Technology Compared with Polysomnography in Adolescents

Zambotti

Baker

Colrain

2015

Sleep

139

106

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1461Validation of a Novel Fitness Tracker to Assess Sleep-de Zambotti et al. INTRODUCTIONPolysomnography (PSG) is the gold standard for evaluating sleep in basic and clinical sleep research. However, there are several disadvantages of this technique; it is expensive, intrusive, time consuming for individuals and evaluators, impractical for long-term in-laboratory and at home recordings, and has limited availability. These limitations have led to the development of other methodologies to objectively evaluate sleep.Wrist actigraphy is firmly established as the main alternative to PSG, particularly in a non-laboratory setting. Actigraphy is based on the assumption that people move most during wake states with a progressive reduction in motion as they approach the deepest stages of sleep, such that sleep/wake states may be discriminated by measuring and analyzing these movements. 1 Actigraph devices are relatively accessible in terms of cost, are easy to use, and can be worn for prolonged period of time (24 h/day for more than 1 w) providing an exhaustive picture of Study Objectives: To evaluate the accuracy in measuring nighttime sleep of a fitness tracker (Jawbone UP) compared to polysomnography (PSG). Design: Jawbone UP and PSG data were simultaneously collected from adolescents during an overnight laboratory recording. Agreements between Jawbone UP and PSG sleep outcomes were analyzed using paired t tests and Bland-Altman plots. Multiple regressions were used to investigate which PSG sleep measures predicted Jawbone UP "Sound sleep" and "Light sleep." Setting: SRI International Human Sleep Laboratory. Participants: Sixty-five healthy adolescents (28 females, mean age ± standard deviation [SD]: 15.8 ± 2.5 y). Interventions: N/A. Measurements and Results: Outcomes showed good agreements between Jawbone UP and PSG for total sleep time (mean differences ± SD: −10.0 ± 20.5 min), sleep efficiency (mean differences ± SD: −1.9 ± 4.2 %), and wake after sleep onset (WASO) (mean differences ± SD: 10.6 ± 14.7 min). Overall, Jawbone UP overestimated PSG total sleep time and sleep efficiency and underestimated WASO but differences were small and, on average, did not exceed clinically meaningful cutoffs of > 30 min for total sleep time and > 5% for sleep efficiency. Multiple regression models showed that Jawbone UP "Sound sleep" measure was predicted by PSG time in N2 (β = 0.25), time in rapid eye movement (β = 0.29), and arousal index (β = −0.34). Jawbone UP "Light sleep" measure was predicted by PSG time in N2 (β = 0.48), time in N3 (β = 0.49), arousal index (β = 0.38) and awakening index (β = 0.28). Jawbone UP showed a progression from slight overestimation to underestimation of total sleep time and sleep efficiency with advancing age. All relationships were similar in boys and girls. Conclusions: Jawbone UP shows good agreement with polysomnography in measures of total sleep time and wake after sleep onset in adolescent boys and girls. Further validation is needed in other age groups and clinical populations before advocat...

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“…The decreasing costs of such technologies together with their increased efficiency and portability create almost limitless possibilities to collect, process and communicate physiological and environmental data from the smart home to different stakeholders such as relatives, health care personnel, social workers etc. [13]. The complexity and special characteristics of SHSEC environments raise new privacy issues that are very different from those related to traditional applications and systems [3,10].…”

Section: Privacy In the Context Of Shsecmentioning

confidence: 99%

“…For instance, the developers used mockups in order to increase the elderly's understanding of how they could use the GiraffPlus robot in their homes. Scenarios for the GiraffPlus system were also used to aid to the communication and to increase the elderly peoples' understanding of the functionality in the system 13 . Despite all these efforts the elderly users, who had the system installed at home, were surprised when they could see what data about them and their homes were collected by the GiraffPlus system (interview with a developer responsible for test sites in Sweden).…”

Section: Principle 6: Visibility and Transparencymentioning

confidence: 99%

Privacy Principles in Design of Smart Homes Systems in Elderly Care

Kolkowska

2015

Lecture Notes in Computer Science

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Abstract. Privacy is considered as a main concern in developing and implementing smart home systems for elderly care (SHSEC). Privacy-by-Design (PbD) can help to ensure privacy in such systems and can support the designers in taking the protection of the privacy into account during the development of such systems. In this paper, we investigate the suitability of the PbD principles (PbDPs) suggested by Cavoukian et al. [1] in the context of SHSEC. This research is conducted as a qualitative case study, where we highlight limitations of existing PbDPs in this context. Based on our findings, we suggest seven additional PbDPs which complement the existing PbDPs and adjust them in the context of SHSEC.

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Monitoring human health behaviour in one's living environment: A technological review

Cited by 121 publications

References 89 publications

Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification

Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification

Validation of Sleep-Tracking Technology Compared with Polysomnography in Adolescents

Privacy Principles in Design of Smart Homes Systems in Elderly Care

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