IoT and AI-Based Application for Automatic Interpretation of the Affective State of Children Diagnosed with Autism.

Researchers developed a smartphone app called PandaSays that works with a robot to help understand emotions in autistic children. The app uses artificial intelligence to recognize feelings and then tells the robot to respond with actions like singing or dancing. The technology worked reliably in testing, but the study doesn't include information about how well it actually helped children or how many families tried it.

This study describes the development and testing of PandaSays, a machine learning mobile application integrated with an Alpha 1 Pro humanoid robot for interpreting emotional states in autistic children. The system uses deep convolutional neural networks to analyze affective states, with the MobileNet model achieving 56.25% accuracy, outperforming ResNet50 and VGG16 architectures. The application communicates with the robot via Bluetooth and Raspberry Pi to trigger responsive actions like singing and dancing. While the technical implementation appears robust, the study lacks clinical validation data, participant demographics, and comparative effectiveness measures against standard interventions.

While technically innovative, clinical utility remains unproven. The modest emotion recognition accuracy and lack of participant outcomes data limit immediate clinical applications. Further research with autistic children is needed to establish therapeutic benefits and safety.

No participant data reported, no clinical outcomes measured, no control group comparisons. Study focuses on technical development rather than therapeutic effectiveness. Emotion recognition accuracy of 56.25% may be too low for reliable clinical use.

In the context in which it was demonstrated that humanoid robots are efficient in helping children diagnosed with autism in exploring their affective state, this paper underlines and proves the efficiency of a previously developed machine learning-based mobile application called PandaSays, which was improved and integrated with an Alpha 1 Pro robot, and discusses performance evaluations using deep convolutional neural networks and residual neural networks. The model trained with MobileNet convolutional neural network had an accuracy of 56.25%, performing better than ResNet50 and VGG16. A strategy for commanding the Alpha 1 Pro robot without its native application was also established and a robot module was developed that includes the communication protocols with the application PandaSays. The output of the machine learning algorithm involved in PandaSays is sent to the humanoid robot to execute some actions as singing, dancing, and so on.

Alpha 1 Pro has its own programming language-Blockly-and, in order to give the robot specific commands, Bluetooth programming is used, with the help of a Raspberry Pi. Therefore, the robot motions can be controlled based on the corresponding protocols. The tests have proved the robustness of the whole solution.