Date of Award

Fall 12-4-2019

Document Type


Degree Name

Master of Science (MS)


Computational and Data Sciences

First Advisor

Soangra, Rahul

Second Advisor

Mohamed Allali

Third Advisor

Niklas Ignasiak

Fourth Advisor

Jo Armour Smith


Falls are the most common cause of injury in older adults. Around one-third of senior citizens (aged 65 or over) experience at least one fall per year, and the frequency increases by 66 percent for those aged over 85 years. Nowadays wearable systems are gaining popularity to perform fall risk assessments and investigating fall events in natural environments. However all commercially existing systems are expensive, thus there is paucity of knowledge to develop and validate inexpensive wearable systems for fall risk assessment in older adults. An early risk of fall assessment could help health care professionals to intervene earlier. This study investigates the processes involved with designing an Inertial Measurement Unit (IMU) including the rational behind the choice of parts and assembly of the board. The final sensor developed (Mini-Logger) was validated for sway acquisition in laboratory setting. Further the novel sensor was tested on healthy adults for its sensitivity with postural sway at 12 different standing conditions. The results from this study could help in development of inexpensive wearable systems which could identify older individuals at risk of falling for proactive fall prevention. Thus reduction in falls will improve the quality of life of older adults and thereby reduce healthcare costs.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.



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