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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Digitally Diagnosing Multiple Developmental Delays Using Crowdsourcing Fused With Machine Learning: Protocol for a

Aditi Jaiswal1, Ruben Kruiper1, Abdur Rasool1

  • 1Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States.

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Summary
This summary is machine-generated.

This study introduces a gamified web system and machine learning (ML) to simultaneously diagnose autism spectrum disorder and attention-deficit/hyperactivity disorder in minors, improving accuracy in pediatric psychiatric conditions.

Keywords:
ADHDASDattention-deficit/hyperactivity disorderautism spectrum disordercrowdsourcingmachine learningprecision health

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Area of Science:

  • Digital health
  • Machine learning applications in pediatrics
  • Behavioral science

Background:

  • Pediatric psychiatric conditions are often underdiagnosed due to cost, distance, and clinician availability.
  • Current digital phenotyping tools for pediatric psychiatric conditions have limitations in feature sets and prediction accuracy.
  • Existing methods often focus on single, binary predictions, leading to uncertain diagnostic outcomes.

Purpose of the Study:

  • To develop a gamified web system for adaptive data collection.
  • To integrate novel crowdsourcing algorithms with ML for behavioral feature extraction.
  • To simultaneously and precisely predict autism spectrum disorder and attention-deficit/hyperactivity disorder.

Main Methods:

  • Gamified web applications to adaptively curate videos of social interactions.
  • Automated ML methods and crowdsourcing algorithms for behavioral feature extraction.
  • ML models for simultaneous classification and adaptive information requests based on data uncertainty.

Main Results:

  • A preliminary web interface has been developed.
  • A feature selection method identified key behavioral features for the gamified approach.
  • The system is designed for precise, simultaneous prediction of multiple conditions.

Conclusions:

  • The development of an AI-powered tool to distinguish conditions like autism spectrum disorder and attention-deficit/hyperactivity disorder is a significant prospect.
  • This approach has the potential to improve diagnostic accuracy for complex pediatric psychiatric conditions.
  • The system aims to address limitations in current diagnostic tools for minors.