Interpersonal synchrony across vocal and lexical modalities in interactions involving children with autism spectrum disorder

Lahiri, Rimita; Nasir, Md.; Kumar, Manoj; Kim, So Hyun; Bishop, Somer; Lord, Catherine; Narayanan, Shrikanth

doi:10.1121/10.0013421

Cited by 5 publications

(7 citation statements)

References 24 publications

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“…The here-presented and other studies (18,27,28,53) show the potential of developing a multivariable prediction model to assist diagnostics of ASD. However, sample sizes in all of these studies are limited, and while they serve as a proof-of-concept, it is paramount to develop and validate such a multivariable prediction model with significantly larger sample sizes.…”

Section: Discussionmentioning

confidence: 78%

“…This increases psychological stress for the affected person and their families (64,65). Recent studies have shown that machine learning algorithms based on automatically extracted features could assist in this process (18,27,28,53). Koehler et al (28) automatically extracted movement parameters from the video recordings of the dyadic interactions analysed here, although in a slightly larger sample.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, a recent study also used parts of ADOS interviews to investigate classification between autistic and non-autistic children based on synchrony of speech features (27). They extracted lexical features and calculated the similarity of the lexical content of the interviews.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated extraction of speech and turn-taking parameters in autism allows for diagnostic classification using a multivariable prediction model

Plank,

Koehler,

Nelson

et al. 2023

Front. Psychiatry

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Autism spectrum disorder (ASD) is diagnosed on the basis of speech and communication differences, amongst other symptoms. Since conversations are essential for building connections with others, it is important to understand the exact nature of differences between autistic and non-autistic verbal behaviour and evaluate the potential of these differences for diagnostics. In this study, we recorded dyadic conversations and used automated extraction of speech and interactional turn-taking features of 54 non-autistic and 26 autistic participants. The extracted speech and turn-taking parameters showed high potential as a diagnostic marker. A linear support vector machine was able to predict the dyad type with 76.2% balanced accuracy (sensitivity: 73.8%, specificity: 78.6%), suggesting that digitally assisted diagnostics could significantly enhance the current clinical diagnostic process due to their objectivity and scalability. In group comparisons on the individual and dyadic level, we found that autistic interaction partners talked slower and in a more monotonous manner than non-autistic interaction partners and that mixed dyads consisting of an autistic and a non-autistic participant had increased periods of silence, and the intensity, i.e. loudness, of their speech was more synchronous.

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Section: Discussionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

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Automated extraction of speech and turn-taking parameters in autism allows for diagnostic classification using a multivariable prediction model

Plank,

Koehler,

Nelson

et al. 2023

Front. Psychiatry

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“…Two commonly used and conceptually overlapping frameworks—behavioral signal processing [ 74 ] and digital phenotyping [ 75 , 76 ]—share important features and aims: acquisition of multimodal and ecologically valid data, selection of analytic methods suited to the acquired data, and development of models to predict clinical course and treatment response. Both approaches have been usefully implemented in psychiatric conditions as diverse as schizophrenia [ 77 ], depression [ 78 , 79 ], anxiety [ 80 , 81 ], and autism spectrum disorder [ 82 , 83 ].…”

Section: Discussionmentioning

confidence: 99%

Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review

Frank¹,

Li²,

Peterson³

et al. 2023

JMIR Ment Health

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Background Smartphones and wearable biosensors can continuously and passively measure aspects of behavior and physiology while also collecting data that require user input. These devices can potentially be used to monitor symptom burden; estimate diagnosis and risk for relapse; predict treatment response; and deliver digital interventions in patients with obsessive-compulsive disorder (OCD), a prevalent and disabling psychiatric condition that often follows a chronic and fluctuating course and may uniquely benefit from these technologies. Objective Given the speed at which mobile and wearable technologies are being developed and implemented in clinical settings, a continual reappraisal of this field is needed. In this scoping review, we map the literature on the use of wearable devices and smartphone-based devices or apps in the assessment, monitoring, or treatment of OCD. Methods In July 2022 and April 2023, we conducted an initial search and an updated search, respectively, of multiple databases, including PubMed, Embase, APA PsycINFO, and Web of Science, with no restriction on publication period, using the following search strategy: (“OCD” OR “obsessive” OR “obsessive-compulsive”) AND (“smartphone” OR “phone” OR “wearable” OR “sensing” OR “biofeedback” OR “neurofeedback” OR “neuro feedback” OR “digital” OR “phenotyping” OR “mobile” OR “heart rate variability” OR “actigraphy” OR “actimetry” OR “biosignals” OR “biomarker” OR “signals” OR “mobile health”). Results We analyzed 2748 articles, reviewed the full text of 77 articles, and extracted data from the 25 articles included in this review. We divided our review into the following three parts: studies without digital or mobile intervention and with passive data collection, studies without digital or mobile intervention and with active or mixed data collection, and studies with a digital or mobile intervention. Conclusions Use of mobile and wearable technologies for OCD has developed primarily in the past 15 years, with an increasing pace of related publications. Passive measures from actigraphy generally match subjective reports. Ecological momentary assessment is well tolerated for the naturalistic assessment of symptoms, may capture novel OCD symptoms, and may also document lower symptom burden than retrospective recall. Digital or mobile treatments are diverse; however, they generally provide some improvement in OCD symptom burden. Finally, ongoing work is needed for a safe and trusted uptake of technology by patients and providers.

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“…were used as features for a Support Vector Machine (SVM) classifier to classify dyads belonging to a mixed (ASD-TD) or non-autistic control (TD-TD) dyad. 36 proposed a data-driven approach to quantify vocal and linguistic synchrony. Vocal synchrony was assessed by extracting spectral features and measuring distances.…”

Section: Related Workmentioning

confidence: 99%

Classifying interpersonal synchronization states using a data-driven approach: implications for social interaction understanding

Yozevitch,

Dahan,

Seada

et al. 2023

Sci Rep

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This study presents a data-driven approach to identifying interpersonal motor synchrony states by analyzing hand movements captured from a 3D depth camera. Utilizing a single frame from the experiment, an XGBoost machine learning model was employed to differentiate between spontaneous and intentional synchrony modes with nearly $$90\%$$ 90 % accuracy. Our findings demonstrate a consistent pattern across subjects, revealing that movement velocity tends to be slower in synchrony modes. These insights support the notion that the relationship between velocity and synchrony is influenced by the cognitive load required for the task, with slower movements leading to higher synchrony in tasks demanding higher cognitive load. This work not only contributes to the limited literature on algorithms for identifying interpersonal synchrony but also has potential implications for developing new metrics to assess real-time human social interactions, understanding social interaction, and diagnosing and developing treatment strategies for social deficits associated with conditions such as Autism Spectrum Disorder.

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Interpersonal synchrony across vocal and lexical modalities in interactions involving children with autism spectrum disorder

Cited by 5 publications

References 24 publications

Automated extraction of speech and turn-taking parameters in autism allows for diagnostic classification using a multivariable prediction model

Automated extraction of speech and turn-taking parameters in autism allows for diagnostic classification using a multivariable prediction model

Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review

Classifying interpersonal synchronization states using a data-driven approach: implications for social interaction understanding

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