Date: Wednesday 16 March 2016
Time: 14:30 - 16:00
Location / Room: Konferenz 1

Chair:
José M. Moya, Technical University of Madrid, ES

Co-Chair:
Giovanni Ansaloni, University of Lugano, CH

This session addresses energy efficiency for ambient intelligence and healthcare. The first part focuses on systems for fall detection and indoor localization in the context of ambient assisted living. The second part is dedicated to methods for cardiovascular monitoring, including low-power real-time diagnosis and efficient communication.

Time	Label	Presentation Title Authors
14:30	7.3.1	A DIGITAL PROCESSOR ARCHITECTURE FOR COMBINED EEG/EMG FALLING RISK PREDICTION Speaker: Valerio Annese, Politecnico di Bari, IT Authors: Valerio F. Annese1, Sabino Loconte1, Marco Crepaldi2, Danilo Demarchi3 and Daniela De Venuto1 1Politecnico di Bari, IT; 2Center for Space Human Robotics (CSHR), Istituto Italiano di Tecnologia, IT; 3Politecnico di Torino, IT Abstract The brain signal anticipates the voluntary movement with patterns that can be detected even 500ms before the occurrence. This paper presents a digital signal processing unit which implements a real-time algorithm for falling risk prediction. The system architecture is designed to operate with digitized data samples from 8 EMG (limbs) and 8 EEG (motor-cortex) channels and, through their combining, provides 1bit outputs for the early detection of unintentional movement. The digital architecture is validated on an FPGA to determine resources utilization, related timing constraints and performance figures of a dedicated real-time ASIC implementation for wearable applications. The system occupies 85.95% ALMs, 43283 ALUTs, 73.0% registers, 9.9% block memory of an Altera Cyclone V FPGA for a processing latency lower than 1ms. Outputs are available in 56ms, within the time limit of 300ms, enabling decision taking for active control. Comparisons between Matlab (used as golden reference) and measured FPGA outputs outline a very low residual numerical error of about 0.012% (worst case) despite the higher float precision of Matlab simulations and losses due to mandatory dataset conversion for validation. Download Paper (PDF; Only available from the DATE venue WiFi)
15:00	7.3.2	DISTRIBUTED-NEURON-NETWORK BASED MACHINE LEARNING ON SMART-GATEWAY NETWORK TOWARDS REAL-TIME INDOOR DATA ANALYTICS Speaker: Hantao Huang, Nanyang Technological University, SG Authors: Hantao Huang, Yuehua Cai and Hao Yu, Nanyang Technological University, SG Abstract Indoor data analytics is one typical example of ambient intelligence with behavior or feature extraction from positioning, power, and lighting data. It can be utilized to help improve comfort level in building and room for occupants. To address dynamic ambient change in a large-scaled space, real-time and distributed data analytics is required on sensor (or gateway) network, which however has limited computing resources. This paper proposes a computationally efficient data analytics by distributed-neuron-network (DNN) based machine learning with application for indoor positioning. It is based on one incremental L2-norm based solver for learning collected WiFi-data at each gateway and is further fused for all gateways in the network to determine the location. Experimental results show that with multiple distributed gateways running in parallel, the proposed algorithm can achieve 50x and 38x speedup during data testing and training time respectively with comparable positioning accuracy, when compared to traditional support vector machine(SVM) method. Download Paper (PDF; Only available from the DATE venue WiFi)
15:30	7.3.3	TOUCH-BASED SYSTEM FOR BEAT-TO-BEAT IMPEDANCE CARDIOGRAM ACQUISITION AND HEMODYNAMIC PARAMETERS ESTIMATION Speaker: Dionisije Sopic, École Polytechnique Fédérale de Lausanne (EPFL), CH Authors: Dionisije Sopic1, Srinivasan Murali2, Francisco Rincón2 and David Atienza1 1École Polytechnique Fédérale de Lausanne (EPFL), CH; 2SmartCardia Inc., Ltd, CH Abstract Among all cardiovascular diseases, congestive heart failure (CHF) has a very high rate of hospitalization and mortality. In order to prevent hospitalization, there is a strong need to identify patients at risk of a CHF event by estimating a set of relevant hemodynamic parameters that will allow physicians to detect its early onset. Today, one of the most popular non-invasive methods to obtain these parameters is through the acquisition of electrocardiogram (ECG) and impedance cardiogram (ICG) by using large hospital systems with electrodes placed on the chest and thorax region. In order to be useful in an ambulatory setting, it is important to obtain an ultra-low power wearable system for acquiring the ICG and ECG, and to detect CHF. In this paper, we present a touch-based ultra-low power device for real-time ICG and ECG signal acquisition, and hemodynamic parameters estimation. We also propose methods for noise cancellation and for calculating the hemodynamic parameters. In addition, a comparative evaluation of susceptibility to different measuring positions is presented. Our proposed design is highly correlated with traditional systems ( > 80%), but able to work with very low power budgets, thus allowing long duration of operation of over four days on a single battery charge. Download Paper (PDF; Only available from the DATE venue WiFi)
15:45	7.3.4	QUANTIFYING THE BENEFITS OF COMPRESSED SENSING ON A WBSN-BASED REAL-TIME BIOSIGNAL MONITOR Speaker: Daniele Bortolotti, Università di Bologna, IT Authors: Daniele Bortolotti1, Bojan Milosevic2, Andrea Bartolini3, Elisabetta Farella2 and Luca Benini3 1Università di Bologna, IT; 2Fondazione Bruno Kessler, IT; 3ETH Zurich, CH Abstract Technology scaling enables today the design of ultra-low power wearable biosensors for continuous vital signal monitoring or wellness applications. Wireless Body Sensor Networks (WBSN) integrate wearable sensing nodes for an accurate measurement of the desired physiological parameter, e.g. Electrocardiogram (ECG), and a personal gateway for the collection and processing of the data. The diffusion of smartphones enables their use as advanced personal gateways, with the ability to provide user interaction features, connectivity and real-time feedback to the user. Both the sensing node(s) and the smartphone are battery powered and resource-constrained devices, hence energy efficiency is one of the key design goals. In this work, we explore the use of compression techniques to improve the efficiency of a wireless ECG wearable monitor. In the presented system, the wearable node is used for bio-signal acquisition, pre-processing and compression, while a smartphone is used for real-time signal reconstruction. The system aims at medical-grade signal quality and the impact of lossy compression is tested on real signals acquired by the node and its effects are evaluated on system- level energy consumption. We analyze performance/energy trade-offs considering online data compression on the wearable device and real-time reconstruction on the smartphone. Our results show that Compressed Sensing pays off only when the SNR requirement is below 20 dB due to the non-ideal sparsity of ECG signal. We propose a hybrid compression scheme based on CS and under-quantization to address these limitations. Download Paper (PDF; Only available from the DATE venue WiFi)
16:00	IP3-8, 53	ENERGY VS. RELIABILITY TRADE-OFFS EXPLORATION IN BIOMEDICAL ULTRA-LOW POWER DEVICES Speaker: Loris Duch, École Polytechnique Fédérale de Lausanne (EPFL), CH Authors: Loris Duch, Pablo Garcia del Valle, David Atienza, Shrikanth Ganapathy and Andreas Burg, École Polytechnique Fédérale de Lausanne (EPFL), CH Abstract State-of-the-art wearable devices such as embedded biomedical monitoring systems apply voltage scaling to lower as much as possible their energy consumption and achieve longer battery lifetimes. While embedded memories often rely on Error Correction Codes (ECC) for error protection, in this paper we explore how the characteristics of biomedical applications can be exploited to develop new techniques with lower power overhead. We then introduce the Dynamic eRror compEnsation And Masking (DREAM) technique, that provides partial memory protection with less area and power overheads than ECC. Different tradeoffs between the error correction ability of the techniques and their energy consumption are examined to conclude that, when properly applied, DREAM consumes 21% less energy than a traditional ECC with Single Error Correction and Double Error Detection (SEC/DED) capabilities. Download Paper (PDF; Only available from the DATE venue WiFi)
16:01	IP3-9, 883	A MACHINE LEARNING APPROACH FOR MEDICATION ADHERENCE MONITORING USING BODY-WORN SENSORS Speaker: Hassan Ghasemzadeh, Washington State University, US Authors: Niloofar Hezar Jaribi, Ramin Fallahzadeh and Hassan Ghasemzadeh, Washington State University, US Abstract One of the most important challenges in current healthcare systems is medication non-adherence, which has irrevocable outcomes. Although many technologies have been developed for medication adherence monitoring, the reliability and cost-effectiveness of these technologies are not well understood to date. This paper presents a medication adherence monitoring system by user-activity tracking based on wrist-band wearable sensors. We develop machine learning algorithms that track wrist motions in real-time and identify medication intake activities. We propose a novel data analysis pipeline to reliably detect medication adherence by examining single-wrist motions. Our system achieves an accuracy of 78.3% in adherence detection without need for medication pillboxes and with only one sensor worn on either of the wrists. The accuracy of our algorithm is only 7.9% lower than a system with two sensors that track motions of both wrists. Download Paper (PDF; Only available from the DATE venue WiFi)
16:02	IP3-10, 190	REQUIREMENTS-CENTRIC CLOSED-LOOP VALIDATION OF IMPLANTABLE CARDIAC DEVICES Speaker: Partha Roop, The University of Auckland, NZ Authors: Weiwei Ai, Nitish Patel and Partha Roop, The University of Auckland, NZ Abstract Implantable medical devices are recommended by physicians to sustain life while improving the overall quality of life of the patients. In spite of the rigorous testing, there have been numerous failures and associated recalls which suggest that completeness of the testing is elusive. We propose a new validation framework based on formal methods for real-time closed-loop validation of medical devices. The proposed approach includes a synchronous observer acting both as an automated oracle and also as a requirements coverage monitor. The observer combines an on-line testing adequacy evaluation module together with a heuristic learning module. This methodology was applied to validate a pacemaker over a virtual heart model. A subset of the requirements was used to test its efficacy. The results show that the proposed methodology can, in real-time, evaluate the test adequacy and hence guide the on-line test case generation to maximize the requirements coverage. Download Paper (PDF; Only available from the DATE venue WiFi)
16:00		End of session Coffee Break in Exhibition Area