Children Activity Recognition: Challenges and Strategies

Hosseini, Anahita; Fazeli, Shayan; Vliet, Eleanne van; Valencia, Lisa; Habre, Rima; Sarrafzadeh, Majid; Bui, Alex A. T.

doi:10.1109/embc.2018.8513320

Cited by 13 publications

(11 citation statements)

References 16 publications

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“…The Los Angeles PRISMS Center BREATHE dataset [21-23] was collected on 16 participants, aged 5 to 15 years, using the BREATHE Kit, an informatics platform designed to monitor multiple exposures, behaviors, and activities in context to identify personal triggers and predict the risk of pediatric asthma exacerbations in real time. Triaxial accelerometry and gyroscope data were collected at 10 Hz using a wrist-worn Motorola Moto 360 Sport smartwatch.…”

Section: Methodsmentioning

confidence: 99%

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data

Li¹,

Habre²,

Deng³

et al. 2019

JMIR Mhealth Uhealth

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Background Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. Objective We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. Methods We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones ( HARuS ) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma ( BREATHE ) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. Results In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. Conclusions In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as exp...

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Section: Methodsmentioning

confidence: 99%

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data

Li¹,

Habre²,

Deng³

et al. 2019

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

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“…During the activities the arm's EMG signals and voices were recorded. Hosseini et al [52] designed an Android smartwatch application to record accelerometer and gyroscope signals in real-time and transfer the anonymous data to a web server for activity prediction. Each child was asked to wear the smartwatch and was instructed to perform six different activities (running, walking, standing, sitting, lying down, and stair climbing).…”

Section: A Daily-life Activitiesmentioning

confidence: 99%

“…This is in addition to written consent from participating children's parents. Different databases, including MMDB [58], MMLA-Math [64], [65], [52] were recorded under the institutional review board (IRB) approvals. IRB is an organization that reviews and approves (or disapproves) any research study involving human subjects 4 .…”

Section: Ethical Perspectivesmentioning

confidence: 99%

“…Paz et al [47] excluded two children as they did not complete all the required neuro-psychological tests during recording. Asking children to read texts and recording their speech Speech and language analysis [11], [12], [23], [25], [28] Speech recorded via telephone channels (TEL) Speech and language analysis [28] Whole-day, naturalistic recordings of children's vocalizations and auditory environments Speech and language analysis [23], [25] Asking children to wear sensors (smartwatch, arm band) and perform different activities as instructed Activity analysis [9], [52] Collecting records/logs from the learner records store of elearning platforms…”

Section: E Verificationmentioning

confidence: 99%

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A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

2022

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The COVID-19 pandemic can be attributed as a main factor to accelerate the current digital transformation and to encourage innovation and technological adoption. Consequently, the care provided to our children, one of the significant aspects of life, needs to be adapted with the life's changes. Children are our future and our most precious resources. They need our attention in all life domains including health, education, safety and social interaction. Nowadays, technologies have been incorporated with machine learning and it has been proven that they are more powerful, reliable and profitable. Machine learning methods have been applied by many children-related studies to generate predictive models for different applications. The efficacy of the generated models mainly rely on the constructed databases. This article carries out a comprehensive survey on available children's databases constructed for machine-learningbased solutions with their methodologies, characteristics, challenges, and applications. First, it provides an overview of the available studies and classifies them based on their applications. Next, it defines a set of attributes and evaluates them while also shedding light on their pros and cons. The primary concerns related to collection, development and distribution of children's databases are also discussed. This study can be considered as a guideline for researchers in multidisciplinary fields to construct reliable databases and to develop more advanced techniques.

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“…This section summarizes human activity recognition challenges and future trends [76,[224][225][226][227][228][229][230][231][232][233][234][235][236][237][238][239]:…”

Section: Challenges and Future Trendsmentioning

confidence: 99%

A Review of Physical Human Activity Recognition Chain Using Sensors

AlShorman¹,

Alshorman²,

Masadeh³

2020

IJEEI

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In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

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Children Activity Recognition: Challenges and Strategies

Cited by 13 publications

References 16 publications

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data

A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

A Review of Physical Human Activity Recognition Chain Using Sensors

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