Haptic Feedback: An Experimental Evaluation of Vibrations as Tactile Sense in Autistic People

One of the most prevalent behavioral impairments in autistic people is difficulty processing sensory information. People commonly observe this phenomenon as either hypersensitivity or hyposensitivity to tactile stimuli. To rectify this irregularity, numerous researchers have suggested wearable sensor-based systems and applications within the realm of virtual environments. However, they have neglected to carry out an adequate evaluation and proof of its feasibility for autistic people. Hence, this study compares three methods to identify the most effective approach to understanding tactile sensory processing in autistic people using haptic technology. The evaluation included behavioral response analysis, which involves observing autistic people; statistical analysis on tactile sensory patterns (TSP), which analyzes data from 9-axis IMU sensors and EMG sensors; and machine learning models, such as recurrent neural networks (RNNs), trained on tactile sensory sensitivity data. The study demonstrates that behavioral response analysis is limited by subjectivity and variability in responses, despite its capacity to provide useful qualitative perspectives. Meanwhile, statistical analysis reveals limitations in its ability to predict sensory outcomes, despite its capacity to provide quantitative measurements of variations in tactile sensory processing. Comparative analysis using machine learning, on the other hand, outperforms both behavioral response analysis and statistical analysis in tactile sensory processing classification and prediction. In particular, the RNN model exhibits remarkable accuracy and correctness in detecting tactile sensory processing among autistic people. This study demonstrated that machine learning can be advantageous for autistic people to analyze tactile sensory processing, explore, and develop touch sensitivity to improve their quality of life.