SafeAccess+: An Intelligent System to make Smart Home Safer and Americans with Disability Act Compliant

TL;DR

SafeAccess+ is an intelligent, ADA-compliant smart home system that enhances safety and independence for people with disabilities by monitoring premises, recognizing visitors, and providing threat assessments.

Contribution

The paper introduces a novel integrated system combining person detection, activity recognition, and ADA compliance for safer smart home environments.

Findings

High accuracy in person and activity recognition

Effective threat assessment and notification system

Excellent user satisfaction and reliability scores

Abstract

Smart homes are becoming ubiquitous, but they are not Americans with Disability Act (ADA) compliant. Smart homes equipped with ADA compliant appliances and services are critical for people with disabilities (i.e., visual impairments and limited mobility) to improve independence, safety, and quality of life. Despite all advancements in smart home technologies, some fundamental design and implementation issues remain. For example, people with disabilities often feel insecure to respond when someone knocks on the door or rings the doorbell. In this paper, we present an intelligent system called "SafeAccess+" to build safer and ADA compliant premises (e.g. smart homes, offices). The key functionalities of the SafeAccess+ are: 1) Monitoring the inside/outside of premises and identifying incoming people; 2) Providing users relevant information to assess incoming threats (e.g., burglary, robbery) and ongoing crimes 3) Allowing users to grant safe access to homes for friends/family members. We have addressed several technical and research challenges: - developing models to detect and recognize person/activity, generating image descriptions, designing ADA compliant end-end system. In addition, we have designed a prototype smart door showcasing the proof-of-concept. The premises are expected to be equipped with cameras placed in strategic locations that facilitate monitoring the premise 24/7 to identify incoming persons and to generate image descriptions. The system generates a pre-structured message from the image description to assess incoming threats and immediately notify the users. The completeness and generalization of models have been ensured through a rigorous quantitative evaluation. The users' satisfaction and reliability of the system has been measured using PYTHEIA scale and was rated excellent (Internal Consistency-Cronbach's alpha is 0.784, Test-retest reliability is 0.939 )