Approaches for Predicting the Occurrence of Challenging Behaviors in Individuals with Autism Spectrum Disorder: A Narrative Review
Abstract
1. ASD and Challenging Behaviors
2. Types and Prevalence of Challenging Behaviors
Behavior Frequency and Co-Occurrence
3. Co-Occurring Conditions and ASD
3.1. Gastrointestinal Conditions
Associations with Behavior
3.2. Epilepsy
Associations with Behavior
3.3. Sleep Disorders
Associations with Behavior
3.4. Allergies and Immune Conditions
Associations with Behavior
3.5. Menses
Associations with Behavior
4. Profound ASD and Challenging Behaviors
Psychological Functions of Behavior
5. State of Behavior Prediction Research
5.1. Wearable Sensor Approaches
5.1.1. Physiological Signals
5.1.2. Movement Sensors
5.1.3. Combined Analysis
5.1.4. Wearable Sensor Limitations
5.2. Non-Wearable Sensors
5.2.1. Bed Sensors
5.2.2. Computer Vision Approaches
5.3. Manually Recorded Approaches
6. Challenging Behavior Intervention and Reduction
6.1. Assessment
6.1.1. Functional Behavior Assessment
6.1.2. Stimulus Assessment
6.2. Pharmacological
6.3. Non-Pharmacological
6.4. Transdisciplinary
7. Limitations of the Field
8. Conclusions and Opportunities for Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ASD | autism spectrum disorder |
SIB | self-injurious behavior |
COC | co-occurring condition |
GI | gastrointestinal |
SMM | stereotypical motor movements |
ADDM | Autism and Developmental Disabilities Monitoring |
CDC | Center for Disease Control |
ID | intellectual disability |
ODD | oppositional defiant disorder |
TD | typically developing |
GERD | gastroesophageal reflux disease |
NT | neurotypical |
FBA | functional behavior analysis |
ECG | electrocardiogram |
EDA | electrodermal analysis |
EMG | electromyography |
SVM | support vector machine |
NS | validation method not stated |
CV | cross validation |
LOOCV | leave-one-out cross validation |
HR | heart rate |
LERS | learning from examples using rough sets |
AUROC | area under the receiver operator curve |
HRV | heart rate variability |
AUC | Area under the curve |
RBF | radial basis function |
CNN | convolutional neural network |
LSTM | long short-term memory |
DT | decision tree |
RF | random forest |
NN | neural network |
KNN | k-nearest neighbors |
LOSOCV | leave-one-subject-out cross validation |
LOTOCV | leave-one-trial-out CV |
HMM | hidden Markov model |
LASSO | least absolute shrinkage and selection operator |
PCA | principal component analysis |
LR | logistic regression |
RNN | recurrent neural network |
IBI | inter-beat interval |
BVP | blood volume pulse |
MLP | multilayer perceptron |
XGBoost | extreme gradient boosting |
RFID | radio frequency identification |
EMFis | electromechanical film sensors |
BCG | ballistocardiogram |
ANN | artificial neural networks |
IR | infrared |
RCNN | recurrent convolutional neural network |
SSBD | self-stimulatory behavior dataset |
TCDN | temporal coherency deep network |
QDA | quadratic discriminant analysis |
GAN | generative adversarial network |
WGAN | Wasserstein distance |
ARRBD | Autism Restricted and Repetitive Behavior Dataset |
MS-RRBR | Multi-Model Synergistic Restricted and Repetitive Behavior Recognition method |
LSTRCN | long short-term recurrent convolutional network |
ADALINE | adaptive linear neuron |
TASD | Text-based Early Autism Spectrum Disorder Detection Dataset for Toddlers |
BERT | bidirectional encoder representations from transformers |
ABA | applied behavior analysis |
PBS | positive behavior support |
DBS | deep brain stimulation |
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Behavior Type | Definition | Examples | Prevalence in Those with ASD | Source |
---|---|---|---|---|
Aggression | Aggressive acts towards other people | Biting, head-butting, threatening, scratching | 51.7–56% | [5,15] |
Self-injurious behavior (SIB) | Acts that harm the individual | Pulling hair, banging head, poking eye | 28.0–42% | [5,15,16] |
Elopement | Leaving an area without permission | Running away, bolting, leaving the room | 32.7–49.1% | [17,18,19] |
Disruptive behavior | Any act that could be considered disruptive or inappropriate for the situation | Yelling, tantrums, inappropriate sexual behaviors | 21.0–36% | [13,20] |
Type of COC | Prevalence in Individuals with ASD | Prevalence Citation | Association with Challenging Behavior |
---|---|---|---|
GI | 9–91% | [9] | Increased aggression and SIB, depending on GI symptoms |
Epilepsy | 11.5–30% | [12,26] | Increased SIB |
Sleep | 13–80% | [9,27] | Increased SIB and aggression, depending on type of sleep disorder |
Allergies and Immune | 11.25% | [28] | Mixed associations |
Study | Sample | Signals Used | Model Used | Results |
---|---|---|---|---|
Conn et al. (2008) [86] | (n = 6) Children with ASD | ECG, EDA, EMG, reports of arousal level | SVM [LOOCV] | Prediction accuracy: 79.5% (anxiety), 85% (liking), 84.3% (engagement) |
Sarabadani et al. (2018) [87] | (n = 15) Children with ASD | ECG, skin conductance, skin temperature, and respiration measures | Majority vote from 5 classifiers [CV, 75:25 train:test] | Positive or negative high-arousal state detection: 78.1% average accuracy |
Van Laarhoven et al. (2021) [88] | (n = 5) Young adults with ASD | Respiration patterns | Spire Stone app [NS] | Significant relationship: 2/5 |
Ferguson et al. (2019) [89] | (n = 8) Adolescents with ASD (age 13–20) | EDA | Statistical analysis [NS] | Anticipatory rise in EDA: 60%, EDA returned to median baseline levels: 45% |
Freeman et al. (1999) [90] | (n = 2) Adult men with developmental disabilities | ECG sensors to detect HR | HR scale [NS] | HR increased after challenging behaviors |
Freeman et al. (2003) [91] | (n = 1) Dataset of individual with intellectual disability | HR, observations | LERS [NS] | Set of rules to predict under what conditions certain types of behavior were likely to occur |
Hoch et al. (2010) [92] | (n = 1) Male child with ASD | HR | General transformation approach to auto-regressive integrated moving average [Baseline found separately] | Significant associations between high or low arousal activities |
Lydon et al. (2023) [93] | (n = 3) Male individuals with ASD and ID | HR (band around the torso) | T-test [NS] | Significant SIB difference for 1 participant |
Nuske et al. (2019) [94] | (n = 13) Preschoolers with ASD | Baseline-corrected HR | Conditional LR [Baseline found separately] | Predict challenging behavior: 0.72 AUROC |
Masino et al. (2019) [95] | (n = 22) Children with ASD | HR and beat-to-beat parameters | RBF kernel SVM model [LOOCV] | Classify stressed and relaxed states: 91% AUC |
Koumpouros (2021) [96] | (n = 20) Children with high-functioning ASD | HR | HR threshold [NS] | ≥1/3 were false or unidentified alarms |
Khullar et al. (2021) [97] | (n = 10) Children with ASD, (n = 5) TD children | HR, galvanic skin response, and skin temperature | Hybrid CNN–LSTM network [10 fold CV] | Meltdown prediction: 96% accuracy |
Bagirathan et al. (2021) [98] | (n = 6) Children with ASD (ages 5–11) | ECG | KNN, SVM, and ensemble classifier [NS] | Like or dislike detection: 81% (HRV, ensemble) |
Study | Sample | Signals Used | Model Used | Results |
---|---|---|---|---|
Albinali et al. (2009) [99,100] | (n = 6) Adolescents with ASD (age 13–20) | Accelerometer | C4.5 classifier [10-fold CV] | SMM detection accuracy: 82.5–96.4% (laboratory), 85.9–93.7% (classroom) |
Goodwin et al. (2014) [101] | (n = 6) Individuals with ASD | Wrist and torso-worn accelerometers | SVM [10 fold CV] | SMM detection: 81.2–99.1% accuracy |
Rad et al. (2016) [102] | (n = 6) Children with ASD | Accelerometer | CNN and transfer learning [LOSOCV] | SMM detection: 0.75 (time 1), 0.48 (time 2) F1 score |
Siddiqui et al. (2021) [103] | (n = 10) Children with ASD | Wrist sensors (accelerometer, gyroscope) | RF, NN, KNN [10-fold CV and LOSOCV] | 91% gesture recognition |
Plötz et al. (2012) [104] | (n = 1) Child with ASD | Wrist and ankle accelerometer | SVM [10-fold CV] | 69.7% accuracy to detect challenging behaviors |
Min et al. (2009) [105] | (n = 2) Children with ASD | Wrist and body sensor, back sensor | Accelerometer [NS] | Average classification accuracy: 95.5% rocking (back), 80.5% flapping (back), 86.5% flapping (wrist) |
Min and Tewfik (2010) [106] | (n = 4) Children with ASD | Accelerometer | Autoregressive model [separate training and test sets] | 92.7% average self-stimulating prediction accuracy |
Min (2017) [107] | (n = 4) Children with ASD | Accelerometer and video | HMM [LOTOCV] | 91.5% self-stimulatory behavioral pattern detection |
Westeyn et al. (2005) [80] | (n = 1) NT adult mimicking behaviors | Accelerometer wrist, waist, and ankle sensor | HMM, isolated and continuous recognition [LOOCV] | Self-stimulatory behavior detection: 90.5% accuracy (isolated), 92.86% accuracy (continuous) |
Cantin-Garside et al. (2020) [108] | (n = 11) Children with ASD (age 5–14) | Variable numbers of accelerometers | Multilevel LR [80:20 train:test] | 74.7% SIB prediction accuracy |
Cantin-Garside et al. (2020) [109] | (n = 11) Children with ASD (age 5–14) | Variable numbers of accelerometers | KNN [6:2:2 train:validate:test] | SIB detection: 93.0% |
Alhaddad et al. (2019) [81] | (n = 5) NT adults (training) and (n = 4) NT children (validation) | Accelerometers in toys | RNN [separate training and testing sets] | 80% F1 score (classify hit, shake, throw, pickup, drop, and idle) |
Study | Sample | Signals Used | Model Used | Results |
---|---|---|---|---|
Goodwin et al. (2019) [111] | (n = 20) Children with ASD | Wrist sensor (EDA, IBI, BVP and accelerometer) | Ridge-regularized LR [5 fold CV] | Aggression prediction AUC: 0.84 (individual), 0.71 (population) |
Alban et al. (2023) [112] | (n = 5) Children with ASD | Acceleration, EDA, skin temperature, HR, and BVP | XGBoost [70:30 train:test] | Challenging behavior detection: 99% accuracy |
Imbiriba et al. (2020) [113] | (n = 20) Children with ASD | EDA, BVP, accelerometer | PCA with RBF kernel SVM [4 and 6-fold CV] | Aggression detection: 98% accuracy |
Imbiriba et al. (2023) [114] | (n = 70) Children with ASD | Wrist sensor (EDA, BVP, accelerometer) augmented by behavioral data | LR [5 fold CV] | Predict aggression 3 min prior: 80% AUROC |
Al Banna et al. (2020) [82] | NT facial expression dataset | Camera, RFID toys, wearable (accelerometer, HR, gyroscope, magnetometer, GPS, pedometer, temperature) | CNN [separate training and testing sets] | CNN emotion detection in images: 78.56% accuracy |
Manu et al. (2024) [115] | (n = 5) Male children with ASD | Acceleration, EDA, HR, BVP, and temperature | XGBoost [NS] | Challenging behavior detection: 91% (accuracy), 88% (F1 score) |
Zheng et al. (2021) [116] | (n = 7) Children with ASD | Microsoft Kinect, sensor hoodie, and wristband (head rotations, BVP, EDA, facial expressions, acceleration, and temperature) | RF, NN (population), RF (individual) [4 fold CV, LOOCV] | Challenging behavior prediction accuracy: 98.51% (individual), 82.36% (population) |
Zwilling et al. (2022) [117] | Adults with ASD (ages 20–40) | Clothing sensors (ECG) | HR threshold and LSTM [NS] | Results not reported yet |
Gonçalves et al. (2012) [118] | (n = 4) Children with ASD | Accelerometer watch, Microsoft Kinect | Statistical methods (accelerometer), gesture recognition (Kinect) [CV] | Hand-flapping stereotypy classification accuracy: 76% (accelerometer), 51% (Kinect) |
Rad et al. (2025) [119] | (n = 11) Adolescent males with ASD (ages 10–20) | EDA, three-lead accelerometer, and skin temperature monitoring | LSTM [10 fold CV] | Challenging behavior detection AUROC: 0.710 (accelerometer), 0.524 (EDA), 0.629 (skin temperature) |
Torrado et al. (2017) [120] | (n = 2) Male children with ASD (age 10) | HR and accelerometer | HR threshold [baseline found separately] | System was effective for intervention |
Study | Sample | Signals Used | Model Used | Results |
---|---|---|---|---|
Alivar et al. (2019) [122] | (n = 2) Individuals with profound ASD | EMFis to detect BCG | SVM and ANN [separate training and testing sets] | ANN using sleep onset latency: 85% accuracy, 79% F1 score |
Alivar et al. (2020) [84] | (n = 2) Male children with ID | EMFis to detect BCG | SVM [70:30 train:test] and ANN [70:15:15 train:validate:test] | Collective behavioral prediction: ≥78% |
Study | Sample | Signals Used | Model Used | Results |
---|---|---|---|---|
Kiarashi et al. (2024) [123] | (n = 14) Individuals with ASD | IR camera | Ensemble voting model [80:20 train:test] | Challenging behavior prediction: 74% accuracy |
Patnam et al. (2017) [83] | (n = 5) TD individuals | Camera | RCNN [15–25% validation] | Meltdown gesture detection: 92% accuracy |
Rajagopalan and Goecke (2014) [124] | SSBD | Video and audio files | Histogram, bag of words [5 and 10-fold CV] | Behavior detection: 76.3% accuracy |
Liang et al. (2021) [125] | SSBD | Video and audio files | TDCN and SVM, TDCN and QDA [5-fold CV] | Behavior detection: 98.3% |
Shanmughapriya and Poojashree (2022) [126] | SSBD | Video and audio files | 3D-CNN and Skeleton Joint features [80:20 train:test] | Accuracy: 83.56% (dataset validation), 65% (cross data) |
Lakkapragada et al. (2022) [127] | SSBD | Video and audio files | MobileNetv2 and LSTM [5 fold CV] | Hand-flapping detection: 84% F1 score |
Alkahtani et al. (2023) [128] | SSBD | Video and audio files | Long-term recurrent CNN [80:20 train:test] | Behavior detection: 96% accuracy |
Kurian and Tripathi (2024) [129] | SSBD and videos of children with ASD | Video and audio files | GAN with WGAN [32:82 samples train:test] | Behavior detection accuracy: 93.75% (train), 88.25% (test) |
Wang et al. (2025) [130] | Video and audio files and text input | ARRBD and text inputs | MS-RRBR [7:2:1 train:validate:test] | Behavior detection accuracy: 94.94% |
Singh et al. (2025) [131] | Enhanced SSBD | Video and audio files | LSTRCN [80:20 train:test] | SIB detection accuracy: 92.62% |
Jarraya et al. (2020) [132] | (n = 13) Children with ASD | Microsoft Kinect | RNN with 3 hidden layers [10 fold CV] | Meltdown detection: 85.8% accuracy |
Jarraya et al. (2021) [133] | (n = 13) Children with ASD | Camera | RF [70:30 train:test] | Meltdown detection: 91% accuracy |
Manocha and Singh (2023) [134] | (n = 5) Individuals with ASD | Camera | Combined 3D CNN and LSTM [80:20 train:test] | 90% detection accuracy (SIB, aggression, and elopement) |
Das et al. (2024) [135] | (n = 6) Children with ASD | Cameras (side and above) | Two-layer binary classification model on generated features [5-fold CV, 50:20:30 train:validate:test] | F1 score: 77% (behavior detection), 53% (3 min prior prediction) |
Zhao et al. (2025) [136] | (n = 83) Children with ASD | Audio and video | Modified CAV-MAE [6173:1867 videos train:test] | FOS-II behavior classification: 0.8590 accuracy, 0.5936 F1 score |
Study | Sample | Signals Used | Model Used | Results |
---|---|---|---|---|
Cohen et al. (2018) [138] | (n = 67) Individuals with low-functioning ASD | Sleep duration, night awakenings, sleep onset and offset, and sleep regularity and efficiency | Linear SVM [10 fold CV] | Significant predictive relationship between sleep and behavior for 81% of individuals |
Ferina et al. (2023) [139] | (n = 80) Individuals with ASD (age < 19) | Behavior, GI, sleep, and environmental data | Direct kernel ADALINE [85:15 train:test] | 15–20% of the cohort reached 80% prediction accuracy |
Kiarashi et al. (2024) [140] | (n = 353) Individuals with ASD | Presence/absence of behavior and seizure data | Autoregressive models [80:20 train:test] | Prediction accuracy: 78.4% (aggression), 80.68% (SIB), 85.43% (elopement) |
Monalin and Rubini (2024) [141] | TASD | Written observations | BERT [80:20 train:test] | Average behavior detection: 88% |
Method | Description | Examples | Potential Behavior Prediction Application |
---|---|---|---|
Assessment | Observations or tests to determine influences and impetuses of challenging behavior | FBA and stimulus assessment | Aspects of assessments may be incorporated into a behavior prediction model |
Pharmacological | Medication-based interventions | Second-generation antipsychotics, mood stabilizers | Could allow for or improve the timing of as-needed medications |
Non-Pharmacological | Interventions which do not use medications | ABA, music therapy, electronic tracking devices | Improve timing of interventions |
Transdisciplinary | Intervention approaches that combine two or more of the above methods | Skill-based treatment combined with antipsychotics, ABA with music therapy | Improved timing of interventions, especially to appropriately pair or stagger approaches |
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Ferina, J.; Dando, E.; Anderson, C.; Foster, J.; Lantz, J.; Hamlin, T.; Hahn, J. Approaches for Predicting the Occurrence of Challenging Behaviors in Individuals with Autism Spectrum Disorder: A Narrative Review. J. Pers. Med. 2025, 15, 453. https://doi.org/10.3390/jpm15100453
Ferina J, Dando E, Anderson C, Foster J, Lantz J, Hamlin T, Hahn J. Approaches for Predicting the Occurrence of Challenging Behaviors in Individuals with Autism Spectrum Disorder: A Narrative Review. Journal of Personalized Medicine. 2025; 15(10):453. https://doi.org/10.3390/jpm15100453
Chicago/Turabian StyleFerina, Jennifer, Emma Dando, Conor Anderson, Jenny Foster, Johanna Lantz, Theresa Hamlin, and Juergen Hahn. 2025. "Approaches for Predicting the Occurrence of Challenging Behaviors in Individuals with Autism Spectrum Disorder: A Narrative Review" Journal of Personalized Medicine 15, no. 10: 453. https://doi.org/10.3390/jpm15100453
APA StyleFerina, J., Dando, E., Anderson, C., Foster, J., Lantz, J., Hamlin, T., & Hahn, J. (2025). Approaches for Predicting the Occurrence of Challenging Behaviors in Individuals with Autism Spectrum Disorder: A Narrative Review. Journal of Personalized Medicine, 15(10), 453. https://doi.org/10.3390/jpm15100453