m-Health and Autism: Recognizing Stress and Anxiety with Machine Learning and Wearables Data

Masino, Aaron J.; Forsyth, D. M.; Nuske, Heather J.; Herrington, John D.; Pennington, Jeffrey W.; Kushleyeva, Yelena; Bonafide, Christopher P.

doi:10.1109/cbms.2019.00144

Cited by 17 publications

(11 citation statements)

References 18 publications

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“…Therefore, we elected to consider a linear model (logistic regression) and two non-linear models (XGBoost and SVM) to compare performance over a range of model capacities. We chose XGBoost and SVM as our non-linear models because they have performed well on many recent biomedical research studies and typically have fewer learning parameters than more complex models (e.g., deep-learning) which reduces the risk of overfitting ( Mani et al, 2014 ; Masino et al, 2019a ; Pang et al, 2019 ; Zabihi et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Supervised Machine Learning Applied to Automate Flash and Prolonged Capillary Refill Detection by Pulse Oximetry

Hunter

Jiang²,

Nishisaki

et al. 2020

Front. Physiol.

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Section: Methodsmentioning

confidence: 99%

Supervised Machine Learning Applied to Automate Flash and Prolonged Capillary Refill Detection by Pulse Oximetry

Hunter

Jiang²,

Nishisaki

et al. 2020

Front. Physiol.

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“…In the detection of stress conditions, Masino et al [29] reached 93% of accuracy. Their dataset was created by collecting heart rate and RR interval measurements during rest and during activities generating stress.…”

Section: Performancesmentioning

confidence: 99%

“…Emotion communication 3 [2,6] Challenging behavior prediction 1 [29,46] Stereotypical motor movements 2 [30,36] Self-injury behavior 1 [7] Aggression to others 1 [14] Healthcare monitoring 1 [28] Autistic people suffer from the moment of crisis due to their incapability of controlling their emotions. These manifestations, called meltdowns, may have consequences very dangerous for them in case of self-injuring behaviors or for other people, in case of aggressive behaviors.…”

Section: Interventionmentioning

confidence: 99%

“…Wearable devices may detect the signal of an initial state of stress and anxiety and be adopted for supporting caregivers in intervening before the challenging behaviors explosion [23]. Masino et al [29] proposed an approach to detect challenging behavior based on Heart Rate (HR) and beat to beat (RR).…”

Section: Challenging Behavior Predictionmentioning

confidence: 99%

“…4. Heart rate and beat to beat (RR) were adopted by Masino et al [29] The motion tracker adopted by [40] was Polhemus Fastrak equipped by a motion sensor to put on the index finger, while the EyeLink 1000 Plus eye tracker was selected to capture eye movements.…”

Section: Rq3-wearable Devicesmentioning

confidence: 99%

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Supporting autism spectrum disorder screening and intervention with machine learning and wearables: a systematic literature review

Francese

Yang

2021

Complex Intell. Syst.

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The number of autism spectrum disorder individuals is dramatically increasing. For them, it is difficult to get an early diagnosis or to intervene for preventing challenging behaviors, which may be the cause of social isolation and economic loss for all their family. This SLR aims at understanding and summarizing the current research work on this topic and analyze the limitations and open challenges to address future work. We consider papers published between 2015 and the beginning of 2021. The initial selection included about 2140 papers. 11 of them respected our selection criteria. The papers have been analyzed by mainly considering: (1) the kind of action taken on the autistic individual, (2) the considered wearables, (3) the machine learning approaches, and (4) the evaluation strategies. Results revealed that the topic is very relevant, but there are many limitations in the considered studies, such as reduced number of participants, absence of datasets and experimentation in real contexts, need for considering privacy issues, and the adoption of appropriate validation approaches. The issues highlighted in this analysis may be useful for improving machine learning techniques and highlighting areas of interest in which experimenting with the use of different noninvasive sensors.

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Agitated Behaviors Detection in Children with ASD Using Wearable Data

Montassar,

Chikhaoui,

Wang

2023

Lecture Notes in Computer Science

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Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit agitated behaviors that can isolate them from their peers. This study aims to examine if wearable data, collected during everyday activities, could effectively detect such behaviors. First, we used the Empatica E4 device to collect real data including Blood Volume Pulse (BVP), Electrodermal Activity (EDA), and Acceleration (ACC), from a 9-years-old male child with autism over 6 months. Second, we analyzed and extracted numerous features from each signal, and employed different classifiers including Support Vector Machine (SVM), Random Forest (FR), eXtreme Gradient Boosting (XGBoost), and TabNet. Our preliminary findings showed good performance in comparison with the state of the art. Notably, XGBoost demonstrated the highest performance in terms of accuracy, precision, recall, and F1-score. The accuracy achieved in this paper using XGBoost is equal to $$80\%$$ 80 % which exceeds previous research.

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m-Health and Autism: Recognizing Stress and Anxiety with Machine Learning and Wearables Data

Cited by 17 publications

References 18 publications

Supervised Machine Learning Applied to Automate Flash and Prolonged Capillary Refill Detection by Pulse Oximetry

Supervised Machine Learning Applied to Automate Flash and Prolonged Capillary Refill Detection by Pulse Oximetry

Supporting autism spectrum disorder screening and intervention with machine learning and wearables: a systematic literature review

Agitated Behaviors Detection in Children with ASD Using Wearable Data

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