Comparison of Manual and Automated Feature Engineering for Daily Activity Classification in Mental Disorder Diagnosis

Adamczyk, Jakub; Malawski, Filip

doi:10.31577/cai_2021_4_850

Cited by 12 publications

(11 citation statements)

References 26 publications

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“…Year of publication, n (%) [27,30,38,48,52,59,63,64,69,81] 10 ( 13) 2022 [19][20][21]23,25,28,41,45,49,54,61,62,68,73,74,77,78] 17 (25) 2021 [22,29,31,33,40,43,44,53,57,60,66,70,71,76,79] 15 (22) 2020 [26,32,34,42,46,47,51,56,…”

Section: References Values Featuresmentioning

confidence: 99%

“…Type of publication, n (%) [19,21,23,[25][26][27][28][29][30]34,[38][39][40][41][42][43][44][45][46][48][49][50][51][52][53][54][56][57][58][59][60][61][64][65][66][69][70][71][73][74][75][77][78][79]81,82,84,86,87] 49 (71) Journal article [20,22,24,…”

Section: References Values Featuresmentioning

confidence: 99%

“…Target condition [19,20,[23][24][25][26][27][28]30,32,[34][35][36][37][38]42,43,[46][47][48][49][50][51][52][53] 12) Wired [81] 1 (1) ANT+ (ANT Wireless) a The number of studies does not add up, as 1 (1%) study has both commercial and noncommercial wearable devices. b The number of studies does not add up, as several studies have used >1 wearable device.…”

Section: References Values N (%) Featuresmentioning

confidence: 99%

“…AI category [19,20,23,25,26,31,33,34,37,39,40,42,46,[49][50][51][52][53][54][55][56][57][58][59][60][61][63][64][65][66][67][69][70][71]73,[75][76][77][78][79][80][81]83,84,86,87] 46 (67) ML a [24,29,32,44,47,62,82...…”

Section: References Studies N (%) Featurementioning

confidence: 99%

“…Ground truth assessment e [19,20,32,[34][35][36]42,43,47,48,62,65,[68][69][70][71]86] 17 (25) MADRS f [23,24,27,28,30,38,52,53,59,60,73,77,83 [27,29,33,37,40,41,45,46,[49][50][51]54,57,58,63,64,66,69,75,76,78,…”

Section: (19) Predictionmentioning

confidence: 99%

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Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

Abd‐Alrazaq¹,

AlSaad²,

Aziz³

et al. 2023

J Med Internet Res

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Background Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services. Objective This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. Methods We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis. Results Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine. Conclusions Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies’ results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.

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