UroSound: A Smartwatch-Based Platform to Perform Non-Intrusive Sound-Based Uroflowmetry.

Leveraging consumer technology such as smartwatches to objectively and remotely assess people with voiding dysfunction could capture unique features for prompt diagnosis of a disease. This paper presents the UroSound, the first platform that performs non-intrusive sound-based uroflowmetry with a smartwatch. We study the feasibility of using a smartwatch to assess how well the urinary tract functions by processing the sound generated when the urine stream hits the water level in the toilet bowl, which can be modelled through the sound envelope. Signal-based features related to the sound envelope were extracted from a smartwatch's built-in microphone. The constructed model achieves a good correlation between acoustic and standard uroflowmetry in terms of the voiding shape and it can extract relevant voiding parameters. This indicates that accurate and remote measurement of the ambulatory characteristics of voiding dysfunction can be achieved with smartwatch-based uroflowmetry. UroSound also facilitates the collection of a voiding diary by measuring multiple uroflows during daytime and nighttime. Finally, the performance of 6 commercial smartwatches was analysed while recording a voiding event. The results demonstrate that the presence of an automatic gain control in the smartwatch microphone has a negative impact on the signal envelope, and should be avoided. Overall, this work demonstrates the potential for the use of smartwatches in the assessment of voiding dysfunction, to deliver more personalized and effective health care at home with less waste of time and resources, in particular in rural or less developed areas where access to a urology specialist is more difficult.

Automatic Classification of Audio Uroflowmetry with a Smartwatch.

Prior work has shown the classification of voiding dysfunctions from uroflowmeter data using machine learning. We present the use of smartwatch audio, collected through the UroSound platform, in order to automatically classify voiding signals as normal or abnormal, using classical machine learning techniques. We train several classification models using classical machine learning and report a maximal test accuracy of 86.16% using an ensemble method classifier. Clinical relevance- This classification task has the potential to be part of an essential toolkit for urology telemedicine. It is especially useful in areas that lack proper medical infrastructure but still host ubiquitous audio capture devices such as smartphones and smartwatches.

Enhancing improved heuristic drift elimination for step-and-heading based pedestrian dead-reckoning systems.

Location based services can improve the quality of patient care and increase the efficiency of the healthcare systems. Among the different technologies that provide indoor positioning, inertial sensors based pedestrian dead-reckoning (PDR) is one of the more cost-effective solutions, but its performance is limited by drift problems. Regarding the heading drift, some heuristics make use of the building's dominant directions in order to reduce this problem. In this paper, we enhance the method known as improved heuristic drift elimination (iHDE) to be implemented in a Step-and-Heading (SHS) based PDR system, that allows to place the inertial sensors in almost any location of the user's body. Particularly, wrist-worn sensors will be used. Tests on synthetically generated and real data show that the iHDE method can be used in a SHS-based PDR without losing its heading drift reduction capability.