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The incorporation of sensor technology allows early diagnosis and effective treatment of ASD in affected children.
FREMONT, CA: The incorporation of sensor technology has enabled the healthcare sector to significantly enhance its therapy and treatment capabilities for autism spectrum disorder (ASD). The patients diagnosed with ASD often face difficulties in social interactions. The condition can be diagnosed as early as three years from birth and might show improvement with intensive intervention.
Early diagnosis can pave the way for early treatment, giving the patient a shot at a better life. It can be facilitated by innovation and the incorporation of robust technology in the intervention process. The advancement of sensing technology in the ASD sector has spurred the development of robust sensors such as eye trackers, movement trackers, electrodermal activity monitors, tactile sensors, vocal prosody, speech detectors, and sleep quality assessment devices.
The role of technology is crucial in addressing the challenges faced by children with ASD. It will help them overcome their limitations by enabling effective intervention at the early stages. Sensor technology allows robust screening and therapy of ASD, leading to better patient outcomes. It utilizes a wide range of parameters for the assessment of physical, emotional, and environmental states.
ASD comprises an entire spectrum of disorders with different degrees of manifestation, enabling the clinicians to understand the emotions, facial expressions, and body language of patients with autism. The sensors allow the acquisition of specific data needed for the identification of ASD symptoms. The data from the sensors can be used to screen the disorder in children.
The early screening techniques will enable the identification of symptoms through the effective monitoring of atypical eye gaze movement, disordered prosody, and quality of sleep. The diagnosis will streamline the treatment of affected children, helping them overcome the challenges and facilitate their learning process.
The integration of modern technology will equip researchers and clinicians to identify the stereotypical behaviors in patients and help them develop appropriate social behavior. Desktop-based sensors are leveraged to track the gaze patterns in children. The trackers are non-invasive and do not restrain the movement of the body. They can be leveraged to form scores, which can be useful for determining the risk of autism.
The recent advances in the field have enabled the placements of compact sensors on the body of the patient. The sensors are designed to gather physiological and biometrical data, which can later be used by the clinicians to develop reports. Clinicians can also leverage accelerometers to identify autism. They can be attached to the wrist and arm to track limb movement, skin temperature, electrodermal activity, and heart rate.
The conventional approaches to ASD diagnosis depend on behavioral studies. However, the utilization of sensors to gather physiological data can be used for developing robust treatment programs for ASD patients. It can facilitate the analysis of motor movement that is typical to ASD, including arm flapping, rocking, and finger movement. Sensor technology can pave the way for simplifying the diagnosis of ASD patients. The recording of the movements can be broken down and analyzed to track the patterns, thus creating an algorithm to identify the probability of the condition.
The technology can be used to bridge the communication gap in children who have ASD. By monitoring the autonomic nervous system, the sensors can help the patients in better communicating their emotions. The biometrical data collected by the sensors can be used to fill the gaps in their communication. Their physiological data, including perspiration, heartbeat, and so on, will enable the clinicians to determine the feelings of the patients.
The ability to effectively express themselves can be life-changing for children affected by autism. The sensor technology enables them to prepare for the inevitable episodes of uncontrolled outbursts. The utilization of data for the development of machine learning (ML) algorithms will allow researchers to develop efficient prediction techniques.
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