198 research outputs found
ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network
Personalized ubiquitous healthcare solutions require energy-efficient
wearable platforms that provide an accurate classification of bio-signals while
consuming low average power for long-term battery-operated use. Single lead
electrocardiogram (ECG) signals provide the ability to detect, classify, and
even predict cardiac arrhythmia. In this paper, we propose a novel temporal
convolutional network (TCN) that achieves high accuracy while still being
feasible for wearable platform use. Experimental results on the ECG5000 dataset
show that the TCN has a similar accuracy (94.2%) score as the state-of-the-art
(SoA) network while achieving an improvement of 16.5% in the balanced accuracy
score. This accurate classification is done with 27 times fewer parameters and
37 times less multiply-accumulate operations. We test our implementation on two
publicly available platforms, the STM32L475, which is based on ARM Cortex M4F,
and the GreenWaves Technologies GAP8 on the GAPuino board, based on 1+8 RISC-V
CV32E40P cores. Measurements show that the GAP8 implementation respects the
real-time constraints while consuming 0.10 mJ per inference. With 9.91
GMAC/s/W, it is 23.0 times more energy-efficient and 46.85 times faster than an
implementation on the ARM Cortex M4F (0.43 GMAC/s/W). Overall, we obtain 8.1%
higher accuracy while consuming 19.6 times less energy and being 35.1 times
faster compared to a previous SoA embedded implementation.Comment: 4 pages, 1 figure, 2 table
Identifying the time profile of everyday activities in the home using smart meter data
Activities are a descriptive term for the common ways households spend their time. Examples include cooking, doing laundry, or socialising. Smart meter data can be used to generate time profiles of activities that are meaningful to householdsâ own lived experience. Activities are therefore a lens through which energy feedback to households can be made salient and understandable. This paper demonstrates a multi-step methodology for inferring hourly time profiles of ten household activities using smart meter data, supplemented by individual appliance plug monitors and environmental sensors. First, household interviews, video ethnography, and technology surveys are used to identify appliances and devices in the home, and their roles in specific activities. Second, âontologiesâ are developed to map out the relationships between activities and technologies in the home. One or more technologies may indicate the occurrence of certain activities. Third, data from smart meters, plug monitors and sensor data are collected. Smart meter data measuring aggregate electricity use are disaggregated and processed together with the plug monitor and sensor data to identify when and for how long different activities are occurring. Sensor data are particularly useful for activities that are not always associated with an energy-using device. Fourth, the ontologies are applied to the disaggregated data to make inferences on hourly time profiles of ten everyday activities. These include washing, doing laundry, watching TV (reliably inferred), and cleaning, socialising, working (inferred with uncertainties). Fifth, activity time diaries and structured interviews are used to validate both the ontologies and the inferred activity time profiles. Two case study homes are used to illustrate the methodology using data collected as part of a UK trial of smart home technologies. The methodology is demonstrated to produce reliable time profiles of a range of domestic activities that are meaningful to households. The methodology also emphasises the value of integrating coded interview and video ethnography data into both the development of the activity inference process
Intelligence at the Extreme Edge: A Survey on Reformable TinyML
The rapid miniaturization of Machine Learning (ML) for low powered processing
has opened gateways to provide cognition at the extreme edge (E.g., sensors and
actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research
field proposes to democratize the use of Machine Learning (ML) and Deep
Learning (DL) on frugal Microcontroller Units (MCUs). MCUs are highly
energy-efficient pervasive devices capable of operating with less than a few
Milliwatts of power. Nevertheless, many solutions assume that TinyML can only
run inference. Despite this, growing interest in TinyML has led to work that
makes them reformable, i.e., work that permits TinyML to improve once deployed.
In line with this, roadblocks in MCU based solutions in general, such as
reduced physical access and long deployment periods of MCUs, deem reformable
TinyML to play a significant part in more effective solutions. In this work, we
present a survey on reformable TinyML solutions with the proposal of a novel
taxonomy for ease of separation. Here, we also discuss the suitability of each
hierarchical layer in the taxonomy for allowing reformability. In addition to
these, we explore the workflow of TinyML and analyze the identified deployment
schemes and the scarcely available benchmarking tools. Furthermore, we discuss
how reformable TinyML can impact a few selected industrial areas and discuss
the challenges and future directions
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
Designing Human-Centered Collective Intelligence
Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the âCloudâ age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence
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