DIGITAL HEARING AID SIGNAL PROCESSING SYSTEM USING ANDROID PHONE

Objective: The objective of this research is to propose an Android-based digital hearing aid signal processing algorithm with following key features:(1) Regenerated audio match the patient-specific pattern of hearing loss, (2) noise reduction, and (3) provide flexibility to the users.Methods: The proposed signal processing algorithm is designed based on the specific hearing loss of the hearing disorder patient using inverse Fouriertransform; besides, noise reduction feature is included in the digital algorithm design as well. Proposed digital algorithm has been implemented intoan Android-based smartphone and its performance has been tested under real-time condition.Results: Simulation results show that the frequency response of the proposed digital hearing aid signal processing algorithm is in agreement withthe initial theoretical design that was carried out based on the hearing impaired patient’s audiogram. The proposed algorithm has been implementedin the Android-based smartphone and tested in real time. Results show that most of the patients are satisfied with the regenerated audio quality.According to patient’s comments, the regenerated audio is clear and the users are allowed to control the volume level. Besides, no obvious hearinglatency can be detected.Conclusion: Audio signals generated by the proposed digital signal processing algorithm show similar audio signal frequency response in boththeoretical design and MATLAB simulation results. The only difference between the design and simulation results is the amplification levels. Theproposed algorithm provides flexibility to the users by allowing them to choose the desired amplification level. In real-time testing, the proposedAndroid-based digital hearing aid is able to reduce noise level from the surrounding and the output processed speech match the patient-specifichearing loss

Characterizing Power Consumption of Dual-Frequency GNSS of a Smartphone

Location service is one of the most widely used features on a smartphone. More and more apps are built based on location services. As such, demand for accurate positioning is ever higher. Mobile brand Xiaomi has introduced Mi 8, the world's first smartphone equipped with a dual-frequency GNSS chipset which is claimed to provide up to decimeter-level positioning accuracy. Such unprecedentedly high location accuracy brought excitement to industry and academia for navigation research and development of emerging apps. On the other hand, there is a significant knowledge gap on the energy efficiency of smartphones equipped with a dual-frequency GNSS chipset. In this paper, we bridge this knowledge gap by performing an empirical study on power consumption of a dual-frequency GNSS phone. To the best our knowledge, this is the first experimental study that characterizes the power consumption of a smartphone equipped with a dual-frequency GNSS chipset and compares the energy efficiency with a single-frequency GNSS phone. We demonstrate that a smartphone with a dual-frequency GNSS chipset consumes 37% more power on average outdoors, and 28% more power indoors, in comparison with a singe-frequency GNSS phone.Comment: Published in IEEE Global Communications Conference (GLOBECOM

Advanced observation and telemetry heart system utilizing wearable ECG device and a Cloud platform

Short lived chest pain episodes of post PCI patients represent the most common clinical scenario treated in the Accidents and Emergency Room. Continuous ECG monitoring could substantially diminish such hospital admissions and related ambulance calls. Delivering community based, easy-To-handle, easy to wear, real time electrocardiography systems is still a quest, despite the existence of electronic electrocardiography systems for several decades. The PATRIOT system serves this challenge via a 12-channel, easy to wear, easy to carry, mobile linked, miniaturized automatic ECG device and a Cloud platform. The system may deliver high quality electrocardiograms of a patient to medical personnel either on the spot or remotely both in a synchronous or asynchronous mode, enhancing autonomy, mobility, quality of life and safety of recently treated coronary artery disease patients

Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics.

Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics