Date: Tuesday 10 March 2020
Time: 14:30 - 16:00
Location / Room: Berlioz

Chair:
Graziano Pravadelli, Università di Verona, IT

Co-Chair:
Vassilis Pavlidis, Aristotle University of Thessaloniki, GR

Novel solutions for healthcare and ambient assistant living: innovative brain-computer interfaces, novel cancer prediction systems and energy-efficient ECG and wearable systems.

Time	Label	Presentation Title Authors
14:30	3.7.1	COMPRESSING SUBJECT-SPECIFIC BRAIN-COMPUTER INTERFACE MODELS INTO ONE MODEL BY SUPERPOSITION IN HYPERDIMENSIONAL SPACE Speaker: Michael Hersche, ETH Zurich, CH Authors: Michael Hersche, Philipp Rupp, Luca Benini and Abbas Rahimi, ETH Zurich, CH Abstract Accurate multiclass classification of electroencephalography (EEG) signals is still a challenging task towards the development of reliable motor imagery brain-computer interfaces (MI-BCIs). Deep learning algorithms have been recently used in this area to deliver a compact and accurate model. Reaching high-level of accuracy requires to store subjects-specific trained models that cannot be achieved with an otherwise compact model trained globally across all subjects. In this paper, we propose a new methodology that closes the gap between these two extreme modeling approaches: we reduce the overall storage requirements by superimposing many subject-specific models into one single model such that it can be reliably decomposed, after retraining, to its constituent models while providing a trade-off between compression ratio and accuracy. Our method makes the use of unexploited capacity of trained models by orthogonalizing parameters in a hyperdimensional space, followed by iterative retraining to compensate noisy decomposition. This method can be applied to various layers of deep inference models. Experimental results on the 4-class BCI competition IV-2a dataset show that our method exploits unutilized capacity for compression and surpasses the accuracy of two state-of-the-art networks: (1) it compresses the smallest network, EEGNet [1], by 1.9x, and increases its accuracy by 2.41% (74.73% vs. 72.32%); (2) using a relatively larger Shallow ConvNet [2], our method achieves 2.95x compression as well as 1.4% higher accuracy (75.05% vs. 73.59%). Download Paper (PDF; Only available from the DATE venue WiFi)
15:00	3.7.2	A NOVEL FPGA-BASED SYSTEM FOR TUMOR GROWTH PREDICTION Speaker: Yannis Papaefstathiou, Aristotle University of Thessaloniki, GR Authors: Konstantinos Malavazos1, Maria Papadogiorgaki1, PAVLOS MALAKONAKIS1 and Ioannis Papaefstathiou2 1TU Crete, GR; 2Aristotle University of Thessaloniki, GR Abstract An emerging trend in the biomedical community is to create models that take advantage of the increasing available computational power, in order to manage and analyze new biological data as well as to model complex biological processes. Such biomedical software applications require significant computational resources since they process and analyze large amounts of data, such as medical image sequences. This paper presents a novel FPGA-based system that implements a novel model for the prediction of the spatio-temporal evolution of glioma. Glioma is a rapidly evolving type of brain cancer, well known for its aggressive and diffusive behavior. The developed system simulates the glioma tumor growth in the brain tissue, which consists of different anatomic structures, by utilizing individual MRI slices. The presented innovative hardware system is more than 60% faster than a high-end server consisting of 20 physical cores (and 40 virtual ones) and more than 28x more energy efficient. Download Paper (PDF; Only available from the DATE venue WiFi)
15:30	3.7.3	AN EVENT-BASED SYSTEM FOR LOW-POWER ECG QRS COMPLEX DETECTION Speaker: Silvio Zanoli, EPFL, CH Authors: Silvio Zanoli1, Tomas Teijeiro1, Fabio Montagna2 and David Atienza1 1EPFL, CH; 2Università di Bologna, IT Abstract One of the greatest challenges in the design of modern wearable devices is energy efficiency. While data processing and communication have received a lot of attention from the industry and academia, leading to highly efficient microcontrollers and transmission devices, sensor data acquisition in medical devices is still based on a conservative paradigm that requires regular sampling at the Nyquist rate of the target signal. This requirement is usually excessive for sparse and highly non-stationary signals, leading to data overload and a waste of resources in the full processing pipeline. In this work, we propose a new system to create event-based heart-rate analysis devices, including a novel algorithm for QRS detection that is able to process electrocardiogram signals acquired irregularly and much below the theoretically-required Nyquist rate. This technique allows us to drastically reduce the average sampling frequency of the signal and, hence, the energy needed to process it and extract the relevant information. We implemented both the proposed event-based algorithm and a state-of-the-art version based on regular Nyquist rate based sampling on an ultra-low power hardware platform, and the experimental results show that the event-based version reduces the energy consumption in runtime up to 15.6 times, while the detection performance is maintained at an average F1 score of 99.5%. Download Paper (PDF; Only available from the DATE venue WiFi)
15:45	3.7.4	SEMI-AUTONOMOUS PERSONAL CARE ROBOTS INTERFACE DRIVEN BY EEG SIGNALS DIGITIZATION Speaker: Daniela De Venuto, Politecnico di Bari, IT Authors: Giovanni Mezzina and Daniela De Venuto, Politecnico di Bari, IT Abstract In this paper, we propose an innovative architecture that merges the Personal Care Robots (PCRs) advantages with a novel Brain Computer Interface (BCI) to carry out assistive tasks, aiming to reduce the burdens of caregivers. The BCI is based on movement related potentials (MRPs) and exploits EEG from 8 smart wireless electrodes placed on the sensorimotor area. The collected data are firstly pre-processed and then sent to a novel Feature Extraction (FE) step. The FE stage is based on symbolization algorithm, the Local Binary Patterning, which adopts end-to-end binary operations. It strongly reduces the stage complexity, speeding the BCI up. The final user intentions discrimination is entrusted to a linear Support Vector Machine (SVM). The BCI performances have been evaluated on four healthy young subjects. Experimental results showed a user intention recognition accuracy of ~84 % with a timing of ~ 554 ms per decision. A proof of concept is presented, showing how the BCI-based binary decisions could be used to drive the PCR up to a requested object, expressing the will to keep it (delivering it to user) or to continue the research. Download Paper (PDF; Only available from the DATE venue WiFi)
16:01	IP1-18, 216	A NON-INVASIVE WEARABLE BIOIMPEDANCE SYSTEM TO WIRELESSLY MONITOR BLADDER FILLING Speaker: Michele Magno, ETH Zurich, CH Authors: Markus Reichmuth, Simone Schuerle and Michele Magno, ETH Zurich, CH Abstract Monitoring of renal function can be crucial for patients in acute care settings. Commonly during postsurgical surveillance, urinary catheters are employed to assess the urine output accurately. However, as with any external device inserted into the body, the use of these catheters carries a significant risk of infection. In this paper, we present a non-invasive method to measure the fill rate of the bladder, and thus the rate of renal clearance, via an external bioimpedance sensor system to avoid the use of urinary catheters, thereby eliminating the risk of infections and improving patient comfort. We design and propose a 4-electrode front-end and the whole wearable and wireless system with low power and accuracy in mind. The results demonstrate the accuracy of the sensors and low power consumption of only 80µW with a duty cycling of 1 acquisition every 5 minutes, which makes this battery-operated wearable device a long-term monitor system. Download Paper (PDF; Only available from the DATE venue WiFi)
16:02	IP1-19, 906	INFINIWOLF: ENERGY EFFICIENT SMART BRACELET FOR EDGE COMPUTING WITH DUAL SOURCE ENERGY HARVESTING Speaker: Michele Magno, ETH Zurich, CH Authors: Michele Magno1, Xiaying Wang1, Manuel Eggimann1, Lukas Cavigelli1 and Luca Benini2 1ETH Zurich, CH; 2Università di Bologna and ETH Zurich, IT Abstract This work presents InfiniWolf, a novel multi-sensor smartwatch that can achieve self-sustainability exploiting thermal and solar energy harvesting, performing computationally high demanding tasks. The smartwatch embeds both a System-on-Chip (SoC) with an ARM Cortex-M processor and Bluetooth Low Energy (BLE) and Mr. Wolf, an open-hardware RISC-V based parallel ultra-low-power processor that boosts the processing capabilities on board by more than one order of magnitude, while also increasing energy efficiency. We demonstrate its functionality based on a sample application scenario performing stress detection with multi-layer artificial neural networks on a wearable multi-sensor bracelet. Experimental results show the benefits in terms of energy efficiency and latency of Mr. Wolf over an ARM Cortex-M4F micro-controllers and the possibility, under specific assumptions, to be self-sustainable using thermal and solar energy harvesting while performing up to 24 stress classifications per minute in indoor conditions. Download Paper (PDF; Only available from the DATE venue WiFi)
16:00		End of session