A Flexible Home Automation System Applied to Elderly Care

Grossi, Ferdinando; Matrella, Guido; Munrai, I. De; Ciampolini, Paolo

doi:10.1109/icce.2007.341320

Cited by 7 publications

(6 citation statements)

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“…The BCI system was integrated into the EIB/Konnex environmental control system of DAT using a configurable network interface designed at the University of Parma, called FEIM (Field Ethernet Interface Module, Grossi et al 2007). Speller types used for BCI acquisitions are both 6 6 6 matrices, which are depicted in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Light on! Real world evaluation of a P300-based brain–computer interface (BCI) for environment control in a smart home

et al. 2012

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Section: Methodsmentioning

confidence: 99%

Light on! Real world evaluation of a P300-based brain–computer interface (BCI) for environment control in a smart home

et al. 2012

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“…The BCI system was integrated into the EIB/Konnex environmental control system of DAT using a configurable network interface designed at the University of Parma, called FEIM - F ield E thernet I nterface M odule (Grossi et al, 2007). …”

Section: Methodsmentioning

confidence: 99%

The Role of the Interplay between Stimulus Type and Timing in Explaining BCI-Illiteracy for Visual P300-Based Brain-Computer Interfaces

Carabalona

2017

Front. Neurosci.

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Visual P300-based Brain-Computer Interface (BCI) spellers enable communication or interaction with the environment by flashing elements in a matrix and exploiting consequent changes in end-user's brain activity. Despite research efforts, performance variability and BCI-illiteracy still are critical issues for real world applications. Moreover, there is a quite unaddressed kind of BCI-illiteracy, which becomes apparent when the same end-user operates BCI-spellers intended for different applications: our aim is to understand why some well performers can become BCI-illiterate depending on speller type. We manipulated stimulus type (factor STIM: either characters or icons), color (factor COLOR: white, green) and timing (factor SPEED: fast, slow). Each BCI session consisted of training (without feedback) and performance phase (with feedback), both in copy-spelling. For fast flashing spellers, we observed a performance worsening for white icon-speller. Our findings are consistent with existing results reported on end-users using identical white×fast spellers, indicating independence of worsening trend from users' group. The use of slow stimulation timing shed a new light on the perceptual and cognitive phenomena related to the use of a BCI-speller during both the training and the performance phase. We found a significant STIM main effect for the N1 component on Pz and PO7 during the training phase and on PO8 during the performance phase, whereas in both phases neither the STIM×COLOR interaction nor the COLOR main effect was statistically significant. After collapsing data for factor COLOR, it emerged a statistically significant modulation of N1 amplitude depending to the phase of BCI session: N1 was more negative for icons than for characters both on Pz and PO7 (training), whereas the opposite modulation was observed for PO8 (performance). Results indicate that both feedback and expertise with respect to the stimulus type can modulate the N1 component and that icons require more perceptual analysis. Therefore, fast flashing is likely to be more detrimental for end-users' performance in case of icon-spellers. In conclusion, the interplay between stimulus type and timing seems relevant for a satisfactory and efficient end-user's BCI-experience.

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“…In recent years, the concept of a smart home has been extended from the simple automation and automatic control of the home appliances to a more complex management of the user interaction with several sensors and actuators deployed in the home environment in order to pursue the users' wellbeing and energy sustainability [1][2][3][4][5][6][7][8][9]. The development of wearable sensors has expanded the possibilities available in this context, pushing research towards new solutions based on behavioral monitoring [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function

Bassoli

Bianchi

Munari

2020

Sensors

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Recent research in wearable sensors have led to the development of an advanced platform capable of embedding complex algorithms such as machine learning algorithms, which are known to usually be resource-demanding. To address the need for high computational power, one solution is to design custom hardware platforms dedicated to the specific application by exploiting, for example, Field Programmable Gate Array (FPGA). Recently, model-based techniques and automatic code generation have been introduced in FPGA design. In this paper, a new model-based floating-point accumulation circuit is presented. The architecture is based on the state-of-the-art delayed buffering algorithm. This circuit was conceived to be exploited in order to compute the kernel function of a support vector machine. The implementation of the proposed model was carried out in Simulink, and simulation results showed that it had better performance in terms of speed and occupied area when compared to other solutions. To better evaluate its figure, a practical case of a polynomial kernel function was considered. Simulink and VHDL post-implementation timing simulations and measurements on FPGA confirmed the good results of the stand-alone accumulator.

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A Flexible Home Automation System Applied to Elderly Care

Cited by 7 publications

References 1 publication

Light on! Real world evaluation of a P300-based brain–computer interface (BCI) for environment control in a smart home

Light on! Real world evaluation of a P300-based brain–computer interface (BCI) for environment control in a smart home

The Role of the Interplay between Stimulus Type and Timing in Explaining BCI-Illiteracy for Visual P300-Based Brain-Computer Interfaces

A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function

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