Since noteworthy events happen only occasionally in any data, it is imperative for smart sensors to learn the norms in data so that authorities can be alerted and appropriate action can be taken at the occurrence of an abnormal or noteworthy event. The aim of this project is to develop algorithms that can learn the norm in terms of a hierarchy of meaningful features from data in an unsupervised and online manner. The application testbed is the problem of automatically tuning cochlear implants (CIs) of patients with severe-to-profound hearing loss by continuously monitoring their speech output. The working hypothesis is that deficiencies in hearing for people with significant hearing loss are reflected in their speech production. This project will develop and use unsupervised, online, and biologically plausible machine learning algorithms to learn feature hierarchies from the speech output data of severely-to-profoundly hearing-impaired patients. The learned feature hierarchy from the speech of a patient will be compared to those learned from the speech of a comparable normal hearing population. Deficiencies in the patient's hearing will be ascertained by identifying the missing or distorted features. Algorithms will be developed to map this information into the signal processing strategies used in CIs to enhance the audibility of speech.

The proposed project promises transformative changes to three major interdisciplinary fields: machine learning and artificial intelligence, healthcare, and sensors. It will transform the traditional ways in which the clinical needs of patients are met. For example, the results of this project will provide doctors with evidence-based practices that will better address the specific needs of individual patients by monitoring each patient around the clock at minimal effort and cost.

Hearing loss is the most common birth defect in the U.S. with slightly over 15,000 new pediatric cases each year and societal losses amounting to $4.6 billion over a lifetime. A proven technology for CI tuning would make a significant difference to the lives of over 1.2 million CI candidates in the U.S. and many more around the world, thereby leading to substantial health and economic benefits to society. Other than CI tuning, the proposed algorithms will be applicable to a variety of monitoring applications within healthcare, such as blood pressure, cerebrospinal fluid pressure, intracavitary pressure of the bladder, etc., and beyond healthcare, such as web, machine health, traffic, etc. Continuous monitoring with wearable and implantable body sensors will increase early detection of emergency conditions and diseases in at-risk patients and also provide a wide range of healthcare services for people with various degrees of cognitive and physical disabilities. Not only the elderly and chronically ill, but also the families in which both parents have to work will benefit from these systems to provide high-quality care services for their babies and children. Finally, the proposed project will integrate diversity by promoting teaching, learning, and interdisciplinary research among underrepresented groups.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
Standard Grant (Standard)
Application #
Program Officer
Sylvia J. Spengler
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Memphis
United States
Zip Code