There is an ever-growing and increasing amount of technology options that use speech recognition software. Currently, the market includes smartphones, computers, and individual smart home personal assistants that allow for hands-free access to this technology. Research studies have explored the utility of these assistive devices for the completion of activities of daily living; however, there is limited research looking at the accuracy of voice recognition software within smart home personal assistants in populations with disordered speech. In persons with amyotrophic lateral sclerosis (ALS), symptoms include changes to motor functions, speech in particular, and it is unknown how some of these devices may respond to their disordered speech. The present study aimed to examine the accuracy of the Amazon Echo to respond appropriately to commands given by dysarthric patients with ALS. Participants were asked to read a variety of commands to an Amazon Echo. The sentences and responses by the Amazon Echo were audio-recorded for transcription and intelligibility ratings, which were then analyzed to look for relationships between intelligibility, auditory-perceptual features of speech, and sentence type. Results revealed there was no significant relationship between command intelligibility and accuracy of response by the Amazon Echo, nor was there a significant relationship between any of the auditory-perceptual ratings and accuracy of response. There was, however, a significant and positive association between conversational intelligibility and accuracy of responses by the Amazon Echo. This study provides support for use of hands-free assistive technology in patients with ALS to aid in the maintenance of quality of life and activities of daily living.

No description available.

The rise of internet connected devices has enabled the home with a vast amount of enhancements to make life more convenient. These internet connected devices can be used to form a community of devices known as the internet of things (IoT). There is great value in IoT devices to promote healthy independent living for older adults. Fall-related injuries has been one of the leading causes of death in older adults. For example, every year more than a third of people over 65 in the U.S. experience a fall, of which up to 30 percent result in moderate to severe injury. Therefore, this thesis proposes an IoT-based fall detection system for smart home environments that not only to send out alerts, but also launches interaction models, such as voice assistance and camera monitoring. Such connectivity could allow older adults to interact with the system without concern of a learning curve. The proposed IoT-based fall detection system will enable family and caregivers to be immediately notified of the event and remotely monitor the individual. Integrated within a smart home environment, the proposed IoT-based fall detection system can improve the quality of life among older adults. Along with the physical concerns of health, psychological stress is also a great concern among older adults. Stress has been linked to emotional and physical conditions such as depression, anxiety, heart attacks, stroke, etc. Increased susceptibility to stress may accelerate cognitive decline resulting in conversion of cognitively normal older adults to MCI (Mild Cognitive Impairment), and MCI to dementia. Thus, if stress can be measured, there can be countermeasures put in place to reduce stress and its negative effects on the psychological and physical health of older adults. This thesis presents a framework that can be used to collect and pre-process physiological data for the purpose of validating galvanic skin response (GSR), heart rate (HR), and emotional valence (EV) measurements against the cortisol and self-reporting benchmarks for stress detection. The results of this framework can be used for feature extraction to feed into a regression model for validating each combination of physiological measurement. Also, the potential of this framework to automate stress protocols like the Trier Social Stress Test (TSST) could pave the way for an IoT-based platform for automated stress detection and management.

No description available.