TY - GEN
T1 - Smart-Cuff
T2 - 13th IEEE International Conference on Pervasive Computing and Communication, PerCom Workshops 2015
AU - Fallahzadeh, Ramin
AU - Pedram, Mahdi
AU - Saeedi, Ramyar
AU - Sadeghi, Bahman
AU - Ong, Michael
AU - Ghasemzadeh, Hassan
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/24
Y1 - 2015/6/24
N2 - Leg swelling produced by retention of fluid in leg tissues is known as peripheral edema, which is regarded as a symptom for various systematic diseases such as heart or kidney failure. In current clinical practice, edema is manually assessed by clinical experts. Such an assessment can often be inaccurate and unreliable especially if it is made by different operators at different times. Despite the importance of monitoring edema for the purpose of evaluating the course of disease or the effect of treatment, quantifying peripheral edema in a continuous and accurate fashion has remained a challenge. In this paper, we propose a wearable real-time platform (namely, Smart-Cuff), which integrates advanced technologies in sensing, computation, and signal processing and machine learning for continuous and real-time edema monitoring in remote and in-home settings. Given that peripheral edema is highly dependent on various contextual attributes such as body posture, we present an activity-sensitive approach to discard erroneous or contextually invalid sensor data in order to meet the requirements of both energy efficiency and quality of information. Examination of our hardware prototype demonstrates the effectiveness of the proposed forcesensitive resistor-based edema sensor (with an R2 of 0.97 for our regression model) as well as the activity monitoring mechanism (over 99% accuracy) that provide the means to perform reliable data sanity check on ankle circumference measurements in a continuous manner.
AB - Leg swelling produced by retention of fluid in leg tissues is known as peripheral edema, which is regarded as a symptom for various systematic diseases such as heart or kidney failure. In current clinical practice, edema is manually assessed by clinical experts. Such an assessment can often be inaccurate and unreliable especially if it is made by different operators at different times. Despite the importance of monitoring edema for the purpose of evaluating the course of disease or the effect of treatment, quantifying peripheral edema in a continuous and accurate fashion has remained a challenge. In this paper, we propose a wearable real-time platform (namely, Smart-Cuff), which integrates advanced technologies in sensing, computation, and signal processing and machine learning for continuous and real-time edema monitoring in remote and in-home settings. Given that peripheral edema is highly dependent on various contextual attributes such as body posture, we present an activity-sensitive approach to discard erroneous or contextually invalid sensor data in order to meet the requirements of both energy efficiency and quality of information. Examination of our hardware prototype demonstrates the effectiveness of the proposed forcesensitive resistor-based edema sensor (with an R2 of 0.97 for our regression model) as well as the activity monitoring mechanism (over 99% accuracy) that provide the means to perform reliable data sanity check on ankle circumference measurements in a continuous manner.
UR - http://www.scopus.com/inward/record.url?scp=84946053416&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946053416&partnerID=8YFLogxK
U2 - 10.1109/PERCOMW.2015.7133994
DO - 10.1109/PERCOMW.2015.7133994
M3 - Conference contribution
AN - SCOPUS:84946053416
T3 - 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015
SP - 57
EP - 62
BT - 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 March 2015 through 27 March 2015
ER -