| Title: |
Automated Classification of Manual Exploratory Behaviors Using Sensorized Objects and Machine Learning: A Preliminary Proof-of-Concept Study |
| Language: |
English |
| Authors: |
Priya Patel (ORCID 0000-0003-4382-0300); Harsh Pandya; Rajiv Ranganathan (ORCID 0000-0002-6924-253X); Mei-Hua Lee (ORCID 0000-0002-6622-0706) |
| Source: |
Journal of Motor Learning and Development. 2024 12(2):386-411. |
| Availability: |
Human Kinetics, Inc. 1607 North Market Street, Champaign, IL 61820. Tel: 800-474-4457; Fax: 217-351-1549; e-mail: info@hkusa.com; Web site: https://journals.humankinetics.com/view/journals/jmld/jmld-overview.xml |
| Peer Reviewed: |
Y |
| Page Count: |
26 |
| Publication Date: |
2024 |
| Document Type: |
Journal Articles; Reports - Research |
| Education Level: |
Higher Education; Postsecondary Education |
| Descriptors: |
Discovery Learning; Toys; Measurement Equipment; Object Manipulation; Motion; Artificial Intelligence; Classification; Accuracy; College Students; Behavior |
| Geographic Terms: |
Michigan |
| DOI: |
10.1123/jmld.2023-0045 |
| ISSN: |
2325-3193; 2325-3215 |
| Abstract: |
Manual exploratory behaviors during object interaction that form the basis of tool use behavior, are mostly qualitatively characterized in terms of their frequency and duration of occurrence. To fully understand their functional and clinical significance, quantitative movement characterization is needed alongside their qualitative analysis. However, there are two challenges in quantifying them--(a) reliably classifying the type of movement and (b) performing this classification on a time series automatically. Here, we propose a machine learning-based classification method to address these challenges. We measured three common exploratory behaviors (object rotation, fingering, and throwing) in college-aged adults using "sensorized objects" that had wireless Inertial Measurement Units embedded in them. We then calculated several statistical features based on linear acceleration and angular velocity data to train machine learning classifiers to identify these behaviors. All classifiers identified the behaviors with a substantially higher accuracy (average accuracy = 84.95 ± 4.16%) than chance level (33.33%). Of all models tested, Support Vector Machine Quadratic, Support Vector Machine Medium Gaussian, and Narrow Neural Network were the best models in classifying the three behaviors (average accuracy = 89.34 ± 0.12%). This classification method shows potential for automating movement characterization of exploratory behaviors, thereby may aid early assessment of neurodevelopmental disorders. |
| Abstractor: |
As Provided |
| Entry Date: |
2024 |
| Accession Number: |
EJ1436865 |
| Database: |
ERIC |