Wearable Assistive Technologies for Autism: Opportunities and Challenges

Abstract

Autism is a lifelong developmental condition that affects how people perceive the world and interact with others. Challenges with typical social engagement, common in the autism experience, can have a significant negative impact on the quality of life of individuals and families living with autism. Recent advances in sensing, intelligent, and interactive technologies can enable new forms of assistive and augmentative technologies to support social interactions. However, researchers have not yet demonstrated effectiveness of these technologies in long-term real-world use. This paper presents an overview of social and sensory challenges of autism, which offer great opportunities and challenges for the design and development of assistive technologies. We review the existing work on developing wearable technologies for autism particularly to assist social interactions, analyse their potential and limitations, and discuss future research directions.

Hidden Markov Models to Estimate the Probability of Having Autistic Children

“…Current and potential uses of AI in healthcare also include dermatology, ophthalmology, radiology, histopathology, and nuclear medicine [38]. Some researches involving the use of intelligent systems applied to autism propose the formulation of diagnostic methods based on magnetic resonance imaging [13], [14], [39]- [48], early prediction approaches from behavioral and developmental measures [49], the use of robots and other AI techniques applied to the therapy processes of ASD children [50]- [57], wearable assistive technologies [58], and approaches to predicting autism risk genes [59], [60].…”

"A Wearable Ring-shaped Inertial System to Identify Action Planning Impairments During Reach-to-grasp Sequences: A Pilot Study"

“…This represents a promising solution for objective and reliable monitoring, assessment, and support [17]. Hence, wearable inertial devices have been used, so far, to acquire and process high-frequency rate data to analyze motion performances within several applications, including daily activity living gestures [18], early Alzheimer's detection [19], Parkinson's disease assessment [20], mild cognitive impairment evaluation [21], and autism spectrum disorder assistance [22].…”

Energy Consumption Balanced Topology Variable Routing Algorithm for WWSN in Disaster Rescue Scenarios

“…Here, we define 5 as the energy balance factor, and its can be expressed in Eq. ( 8): 8) where "_@5=0"C and #_@5=0"C are the remaining energy of the wearable sensor node and , respectively; "_' ( "# , "# )is the energy consumed to transmit the packet of "# bits; #_-( "# ) is the energy consumed by the sensor node to receive the packet of "# bits; and α and β are weight coefficients. In traditional energy consumption balanced routing algorithms, the node with the maximum energy is selected as the next hop.…”

“…With the rapid development of wearable technology, wearable wireless sensor networks (WWSN) have emerged in recent years [1][2][3][4][5]. Users in the network can carry one or more wearable devices (including sensor nodes), which can monitor various physiological indices of the human body and information from the surrounding environment, as well as communicate with other users [6][7][8][9][10][11]. The network is stable, effi-cient, and easy to deploy, and can be used as an emergency network in disaster and emergency situations to accurately predict and manage rescue operations and ensure the safety of personnel [12][13][14].…”