6 research outputs found
Using periodicity intensity to detect long term behaviour change
This paper introduces a new way to analyse and
visualize quantified-self or lifelog data captured from
any lifelogging device over an extended period of time.
The mechanism works on the raw, unstructured lifelog
data by detecting periodicities, those repeating patters
that occur within our lifestyles at different frequencies
including daily, weekly, seasonal, etc. Focusing on the
24 hour cycle, we calculate the strength of the 24-hour
periodicity at 24-hour intervals over an extended period
of a lifelog. Changes in this strength of the 24-hour
cycle can illustrate changes or shifts in underlying
human behavior. We have performed this analysis on
several lifelog datasets of durations from several weeks
to almost a decade, from recordings of training
distances to sleep data. In this paper we use 24 hour
accelerometer data to illustrate the technique, showing
how changes in human behavior can be identified
Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption
People are living longer than ever due to advances in healthcare, and this
has prompted many healthcare providers to look towards remote patient care as a
means to meet the needs of the future. It is now a priority to enable people to
reside in their own homes rather than in overburdened facilities whenever
possible. The increasing maturity of IoT technologies and the falling costs of
connected sensors has made the deployment of remote healthcare at scale an
increasingly attractive prospect. In this work we demonstrate that we can
measure the consistency and regularity of the behaviour of a household using
sensor readings generated from interaction with the home environment. We show
that we can track changes in this behaviour regularity longitudinally and
detect changes that may be related to significant life events or trends that
may be medically significant. We achieve this using periodicity analysis on
water usage readings sampled from the main household water meter every 15
minutes for over 8 months. We utilise an IoT Application Enablement Platform in
conjunction with low cost LoRa-enabled sensors and a Low Power Wide Area
Network in order to validate a data collection methodology that could be
deployed at large scale in future. We envision the statistical methods
described here being applied to data streams from the homes of elderly and
at-risk groups, both as a means of early illness detection and for monitoring
the well-being of those with known illnesses.Comment: 2019 2nd International Conference on Sensors, Signal and Image
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Behavioral periodicity detection from 24h wrist accelerometry and associations with cardiometabolic risk and health-related quality of life
Periodicities (repeating patterns) are observed in many human behaviors. Their strength may capture untapped patterns that incorporate sleep, sedentary, and active behaviors into a single metric indicative of better health. We present a framework to detect periodicities from longitudinal wrist-worn accelerometry data. GENEActiv accelerometer data were collected from 20 participants (17 men, 3 women, aged 35–65) continuously for (range: 13.9 to 102.0) consecutive days. Cardiometabolic risk biomarkers and health-related quality of life metrics were assessed at baseline. Periodograms were constructed to determine patterns emergent from the accelerometer data. Periodicity strength was calculated using circular autocorrelations for time-lagged windows. The most notable periodicity was at 24 h, indicating a circadian rest-activity cycle; however, its strength varied significantly across participants. Periodicity strength was most consistently associated with LDL-cholesterol (’s = 0.40–0.79, ’s < 0.05) and triglycerides (’s = 0.68–0.86, ’s < 0.05) but also associated with hs-CRP and health-related quality of life, even after adjusting for demographics and self-rated physical activity and insomnia symptoms. Our framework demonstrates a new method for characterizing behavior patterns longitudinally which captures relationships between 24 h accelerometry data and health outcomes
Behavioral Periodicity Detection from 24 h Wrist Accelerometry and Associations with Cardiometabolic Risk and Health-Related Quality of Life
abstract: Periodicities (repeating patterns) are observed in many human behaviors. Their strength may capture untapped patterns that incorporate sleep, sedentary, and active behaviors into a single metric indicative of better health. We present a framework to detect periodicities from longitudinal wrist-worn accelerometry data. GENEActiv accelerometer data were collected from 20 participants (17 men, 3 women, aged 35–65) continuously for 64.4±26.2 (range: 13.9 to 102.0) consecutive days. Cardiometabolic risk biomarkers and health-related quality of life metrics were assessed at baseline. Periodograms were constructed to determine patterns emergent from the accelerometer data. Periodicity strength was calculated using circular autocorrelations for time-lagged windows. The most notable periodicity was at 24 h, indicating a circadian rest-activity cycle; however, its strength varied significantly across participants. Periodicity strength was most consistently associated with LDL-cholesterol (r’s = 0.40–0.79, P’s < 0.05) and triglycerides (r’s = 0.68–0.86, P’s < 0.05) but also associated with hs-CRP and health-related quality of life, even after adjusting for demographics and self-rated physical activity and insomnia symptoms. Our framework demonstrates a new method for characterizing behavior patterns longitudinally which captures relationships between 24 h accelerometry data and health outcomes.The article is published at https://www.hindawi.com/journals/bmri/2016/4856506