Non-Invasive Bladder Volume Sensing for Neurogenic Bladder Dysfunction Management

Abstract

Many patients who suffer from spinal cord injuries (SCI) also suffer from neurogenic bladder dysfunction, and lack the sensation and control of their bladder. In order to alleviate the build up of bladder pressure from urine production and promote good renal health, it is recommended to perform clean intermittent catheterization (CIC) every 2 to 4 hours throughout the day. However, since urine production is not constant, sometimes the bladder will fill with urine to capacity before the recommended CIC time causing the patient to leak, adding unnecessary embarrassment. As such, incontinence is the primary concern of many SCI patients. Sadly, there are no practical solutions available on the market that addresses this concern. In this work, we investigate using near-infrared spectroscopy to develop a wearable and non-invasive bladder volume sensing system to provide timely alerts to SCI patients based on their current bladder volume. We showcase the feasibility of such a system using an optical phantom that mimics the bladder and by performing ex vivo measurements on a pig bladder and intestines.

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IEEE

Transabdominal Fetal Pulse Oximetry: The Case of Fetal Signal Optimization


Abstract

Current technology used for monitoring fetal well-being has been ineffective at reducing rates of harm to the fetus during the intrapartum period, yet its adoption has significantly increased the number of emergency C-sections performed. Transabdominal fetal pulse oximetry (TFO) aims to reduce the number of surgical interventions through non-invasive measurements of fetal oxygen saturation. When developing an optode for TFO, it is important to select design parameters that will maximize the measurement of the fetal signal. In this paper, we optimize the source-detector distance and wavelengths through Monte Carlo simulations using a multi-layered tissue model for various fetal depths. The results were validated by developing an optical probe with two wavelengths of light to observe pulsating arterial tissue through an optical phantom that mimics the maternal abdomen as a step towards oximetry. Our results show that 735nm and 850nm seem to be the optimal selection of peak wavelengths of light sources to obtain a stronger fetal signal for the fetal depths between 2-5 cm. Improving the signal sensitivity is approached by increasing the spacing between the source and detector, and is limited by the noise-equivalent power of the detector.

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IEEE

A Data-Driven Approach to Pre-Operative Evaluation of Lung Cancer Patients

Abstract

Many early stage lung cancer patients have resectable tumors, however, their cardiopulmonary function needs to be properly evaluated before they are deemed operative candidates. Such patients are typically asked to undergo standard pulmonary function tests, including cardiopulmonary exercise tests (CPET) or stair climbs. The standard tests are conducted only at selected healthcare provider locations, and are labor intensive. In addition, they are sometimes ineffective due to patient co-morbidities, such as limited mobility, which limits patient participation. To address these shortcomings, we envision that cardiopulmonary function can be evaluated in the patient’s environment using an inexpensive wearable device during routine physical activities. We present a cloud-connected mask that is fitted with CO 2 , O 2 , flow volume, and accelerometer sensors. The data collected from the device is transmitted to a cloud service, which facilitates utilization of various data mining algorithms for extraction of insights from the data. As a necessary first step toward cardiopulmonary function evaluation, we study automatic recognition of the user’s physical activity from mask sensors data via an empirical analysis of several data representation and classification algorithms. The results demonstrate accurate activity recognition using mask sensors, and underscore the potential of our approach for cardiopulmonary function evaluation.

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IEEE

Machine Intelligence on Resource-Constrained IoT Devices: The Case of Thread Granularity Optimization for CNN Inference

Abstract

Despite their remarkable performance in various machine intelligence tasks, the computational intensity of
Convolutional Neural Networks (CNNs) has hindered their widespread utilization in resource-constrained
embedded and IoT systems. To address this problem, we present a framework for synthesis of efficient CNN
inference software targeting mobile SoC platforms. We argue that thread granularity can substantially impact the performance and energy dissipation of the synthesized inference software, and demonstrate that
launching the maximum number of logical threads, often promoted as a guiding principle by GPGPU practitioners, does not result in an efficient implementation for mobile SoCs. We hypothesize that the runtime of a
CNN layer on a particular SoC platform can be accurately estimated as a linear function of its computational
complexity, which may seem counter-intuitive, as modern mobile SoCs utilize a plethora of heterogeneous
architectural features and dynamic resource management policies. Consequently, we develop a principled
approach and a data-driven analytical model to optimize granularity of threads during CNN software synthesis. Experimental results with several modern CNNs mapped to a commodity Android smartphone with a
Snapdragon SoC show up to 2.37X speedup in application runtime, and up to 1.9X improvement in its energy
dissipation compared to existing approaches.

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PDF

ACM

Fast and Energy-Efficient CNN Inference on IoT Devices

Abstract

Convolutional Neural Networks (CNNs) exhibit remarkable performance in various machine learning tasks. As
sensor-equipped internet of things (IoT) devices permeate into
every aspect of modern life, it is increasingly important to
run CNN inference, a computationally intensive application, on
resource constrained devices. We present a technique for fast
and energy-efficient CNN inference on mobile SoC platforms,
which are projected to be a major player in the IoT space. We
propose techniques for efficient parallelization of CNN inference
targeting mobile GPUs, and explore the underlying tradeoffs.
Experiments with running Squeezenet on three different mobile
devices confirm the effectiveness of our approach. For further
study, please refer to the project repository available on our
GitHub page: https://github.com/mtmd/Mobile_ConvNet.

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Arxiv

CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android

Abstract

Many mobile applications running on smartphones and wearable devices would potentially benefit from the accuracy and
scalability of deep CNN-based machine learning algorithms.
However, performance and energy consumption limitations
make the execution of such computationally intensive algorithms on mobile devices prohibitive. We present a GPUaccelerated library, dubbed CNNdroid, for execution of
trained deep CNNs on Android-based mobile devices. Empirical evaluations show that CNNdroid achieves up to 60X
speedup and 130X energy saving on current mobile devices.
The CNNdroid open source library is available for download
at https://github.com/ENCP/CNNdroid

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ACM

Towards data-driven pre-operative evaluation of lung cancer patients: the case of smart mask

Abstract

Lung cancer is the number one cause of cancer deaths. Many early stage lung cancer patients have a resectable tumor, however, their cardiopulmonary function needs to be properly evaluated before they are deemed operative candidates. Pulmonary function is assessed via spirometry and diffusion capacity. If these are below a certain threshold, cardiopulmonary exercise testing (CPET) is recommended. CPET is expensive, labor intensive, and sometimes ineffective since the patient is unable to fully participate due to comorbidities, such as limited mobility. In addition, CPET is done using a set of physical activities that may or may not be relevant to the patient’s typical activities. This paper presents steps towards developing a solution to address this gap. Specifically, we present OOCOO, a mobile mask system designed to measure oxygen and carbon dioxide levels in respiration, as well as activity levels. Unlike state of practice, oxygen, carbon dioxide, and activity data can be continuously measured over a long period of time in the patient’s environment of choice. The mask is capable of wireless data transfer to commodity smartphones. We have carried out initial work on development of an Android application to capture, analyze, and share the data with authorized entities.

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IEEE

Wearable Sensor Selection, Motion Representation and their Effect on Exercise Classification

Abstract

Motion classification using accelerometer, gyroscope and magnetometer sensors have been an important area of exploration for the past decade. Mostly studied in the context of health related applications, the implications of accurate inertial-magnetic motion classification span from continuous daily activity monitoring and remote assessment of patients recovery to athlete optimization and entertainment applications. While much has been done to optimize classification and segmentation algorithms, very little is understood of the effect sensor selection and motion representation has on overall system performance. In this paper, three sensors (accelerometer, gyroscope, orientation), seven motion representations and six classification techniques (K Nearest Neighbor, Artificial Neural Networks, Random Forests, Support Vector Machines, Naive Bayes) are compared. In addition to traditional time domain motion representations, a novel space domain representation is put forth which results in a two order of magnitude reduction in computational complexity. A case study dataset is created from 11 individuals performing 10 repetitions of 10 different upper body exercises. A single bicep mounted smart phone is used for data collection and both action classification and non-action rejection ability are studied.

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IEEE