Wearable data analysis, visualisation and recommendations on the go using android middleware

Angelides, Marios C.; Wilson, Lissette Andrea Cabello; Echeverría, Paola Liliana Burneo

doi:10.1007/s11042-018-5867-y

Cited by 20 publications

(21 citation statements)

References 21 publications

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“…All included studies used accelerometer sensors. We could categorize these devices into three groups: (1) commercial wrist-worn wearable accelerometers that are consumer-grade devices with a sample rate between 30 Hz and 60 Hz, such as Fitbits [ 13 , 14 , 19 , 22 , 25 ], Samsung Gear [ 17 ], and Nokia [ 15 ]; (2) smartphone accelerometers with a sample rate usually set to 50 Hz and up to 100 Hz, in which data were collected via an app installed in the smartphone [ 7 , 9 , 10 , 16 , 18 ]; and (3) scientifically validated wearable accelerometers with a sample rate up to 100 Hz, such as ActiGraph [ 8 ], GENEActiv [ 11 , 24 ], and other devices developed for health care [ 12 , 20 ].…”

Section: Resultsmentioning

confidence: 99%

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Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Díaz¹,

Caillaud²,

Yacef³

2023

JMIR Med Inform

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Background Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants’ physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants’ physical activity evolves. Objective The aim of this systematic review was to identify and present the various data mining techniques employed to analyze changes in physical activity behaviors from sensors-derived data in health education and health promotion intervention studies. We addressed two main research questions: (1) What are the current techniques used for mining physical activity sensor data to detect behavior changes in health education or health promotion contexts? (2) What are the challenges and opportunities in mining physical activity sensor data for detecting physical activity behavior changes? Methods The systematic review was performed in May 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We queried the Association for Computing Machinery (ACM), IEEE Xplore, ProQuest, Scopus, Web of Science, Education Resources Information Center (ERIC), and Springer literature databases for peer-reviewed references related to wearable machine learning to detect physical activity changes in health education. A total of 4388 references were initially retrieved from the databases. After removing duplicates and screening titles and abstracts, 285 references were subjected to full-text review, resulting in 19 articles included for analysis. Results All studies used accelerometers, sometimes in combination with another sensor (37%). Data were collected over a period ranging from 4 days to 1 year (median 10 weeks) from a cohort size ranging between 10 and 11615 (median 74). Data preprocessing was mainly carried out using proprietary software, generally resulting in step counts and time spent in physical activity aggregated predominantly at the daily or minute level. The main features used as input for the data mining models were descriptive statistics of the preprocessed data. The most common data mining methods were classifiers, clusters, and decision-making algorithms, and these focused on personalization (58%) and analysis of physical activity behaviors (42%). Conclusions Mining sensor data offers great opportunities to analyze physical activity behavior changes, build models to better detect and interpret behavior changes, and allow for personalized feedback and support for participants, especially where larger sample sizes and longer recording times are available. Exploring different data aggregation levels can help detect subtle and sustained behavior changes. However, the literature suggests that there is still work remaining to improve the transparency, explicitness, and standardization of the data preprocessing and mining processes to establish best practices and make the detection methods easier to understand, scrutinize, and reproduce.

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Section: Resultsmentioning

confidence: 99%

“…In 7 out of the 19 (37%) selected studies, accelerometers were used with other sensors such as GPS tracking [ 10 , 16 , 17 ], compass position tracking [ 17 , 20 ], heart rate trackers [ 17 , 21 ], and smart scales [ 15 , 19 ].…”

Section: Resultsmentioning

confidence: 99%

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Díaz¹,

Caillaud²,

Yacef³

2023

JMIR Med Inform

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“…The analytical usage of this data, as it has been discussed throughout this current review article, depends on approaches to access and distribute the data. The development of wearables-associated software directed at both accurate health monitoring and state of the art data collection, analysis and visualization is of the utmost importance [ 89 ]. Both standalone and hybrid methods for anomaly detection require accurate interpretations that can be correlated with the user activity and their daily experience.…”

Section: Prospectsmentioning

confidence: 99%

“…Another important area is the transparency of algorithms designed to compute steps or sleep data, which should be encouraged. The limitations of the large-scale data generated from wearables need to be addressed more often [ 89 ].…”

Section: Prospectsmentioning

confidence: 99%

Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects

Sunny¹,

Patro²,

Karnani³

et al. 2022

Sensors

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Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.

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“…[ 12 ] generated daily personalised text messages with custom timing, frequency, and feedback about their step count/goal and with motivational content to support reflection using a multiarmed bandit (MAB) algorithm and the number of minutes spent in PA. Ref. [ 13 ] used a genetic algorithm, pareto-optimality, and the participants’ daily sleep duration, steps, calories, exercise duration, exercise distance, exercise calories, step count, step distance, and step calories to analyse the wearer’s data and make personal lifestyle improvement recommendations. Ref.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data

Díaz

Caillaud

Yacef

2022

Sensors

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Wearable accelerometers record physical activity with high resolution, potentially capturing the rich details of behaviour changes and habits. Detecting these changes as they emerge is valuable information for any strategy that promotes physical activity and teaches healthy behaviours or habits. Indeed, this offers the opportunity to provide timely feedback and to tailor programmes to each participant’s needs, thus helping to promote the adherence to and the effectiveness of the intervention. This article presents and illustrates U-BEHAVED, an unsupervised algorithm that periodically scans step data streamed from activity trackers to detect physical activity behaviour changes to assess whether they may become habitual patterns. Using rolling time windows, current behaviours are compared with recent previous ones, identifying any significant change. If sustained over time, these new behaviours are classified as potentially new habits. We validated this detection algorithm using a physical activity tracker step dataset (N = 12,798) from 79 users. The algorithm detected 80% of behaviour changes of at least 400 steps within the same hour in users with low variability in physical activity, and of 1600 steps in those with high variability. Based on a threshold cadence of approximately 100 steps per minute for standard walking pace, this number of steps would suggest approximately 4 and 16 min of physical activity at moderate-to-vigorous intensity, respectively. The detection rate for new habits was 80% with a minimum threshold of 500 or 1600 steps within the same hour in users with low or high variability, respectively.

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Wearable data analysis, visualisation and recommendations on the go using android middleware

Cited by 20 publications

References 21 publications

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data

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