Sponsor: National Science Foundation

Group: Dong Ha

Recent efforts to improve fall detection in the elderly population may help prevent the nightmare scenario of an injured loved one lying helpless for an extended period. However, once a fall event has occurred, outcomes are typically poor and costs are high, regardless of how quickly caretakers detect and respond to falls. Fall prevention is therefore the key to health preservation. The research investigates to develop a portable fall prediction monitoring system for early detection of fall risks that can provide early diagnosis / treatment before a fall occurs to reduce long-term health effects and injuries. Users would wear the device as a faux piece of jewelry on a piece of clothing or around an ankle. It will measure potentially small declining increments in gait, posture and mobility of a patient, major indictors that can help point to a future fall.

The research project involves four different teams across different disciplines, Prof. Thurmon Lockhart (PI, Industry and Systems Eng.), Prof. Karen Roberto (Center for Gerontology), and Prof. John Lach (Electrical and Computer Eng., Univ. of Virginia) and is funded by NSF's Smart Health and Wellbeing Program ($1.2M for four years). Our team develops a wearable wireless sensor node, which senses the motion of a person and transmits the data to a host computer wirelessly. The major design object is to reduce the power dissipation of the sensor node without compromising the quality of key data captured.

Course: Analog and RF IC Tracks

Sensor Nodes