Subjectively Optimal Delay in Computer Response to a User's Voluntary Action

2004 ◽  
Vol 99 (3) ◽  
pp. 924-930 ◽  
Author(s):  
Hiroshi Nittono ◽  
Koichi Shimizu ◽  
Tadao Hori
Author(s):  
Severin Schroeder

One aspect of Schopenhauer’s doctrine that the world is will, which can be assessed independently of his more ambitious metaphysical ideas, is the claim that our own agency provides us with a full understanding of causation which then permeates and structures our experience of the world in general. In this chapter, the author argues that this claim can be defended against Hume’s well-known objections because they are based on a volitional theory of voluntary action, which Schopenhauer rightly rejected. Schopenhauer quite plausibly located an immediate experience of causation between at least some kinds of motives and our consequent actions. However, he was wrong in suggesting that this experience might be the source of our understanding of causation since intentional action already presupposes that understanding and cannot provide it. It is more plausible to argue that an understanding of causation is derived from our bodily encounters with material objects.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aline dos Santos Silva ◽  
Hugo Almeida ◽  
Hugo Plácido da Silva ◽  
António Oliveira

AbstractMultiple wearable devices for cardiovascular self-monitoring have been proposed over the years, with growing evidence showing their effectiveness in the detection of pathologies that would otherwise be unnoticed through standard routine exams. In particular, Electrocardiography (ECG) has been an important tool for such purpose. However, wearables have known limitations, chief among which are the need for a voluntary action so that the ECG trace can be taken, battery lifetime, and abandonment. To effectively address these, novel solutions are needed, which has recently paved the way for “invisible” (aka “off-the-person”) sensing approaches. In this article we describe the design and experimental evaluation of a system for invisible ECG monitoring at home. For this purpose, a new sensor design was proposed, novel materials have been explored, and a proof-of-concept data collection system was created in the form of a toilet seat, enabling ECG measurements as an extension of the regular use of sanitary facilities, without requiring body-worn devices. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard equipment, involving 10 healthy subjects. For the acquisition of the ECG signals on the toilet seat, polymeric electrodes with different textures were produced and tested. According to the results obtained, some of the textures did not allow the acquisition of signals in all users. However, a pyramidal texture showed the best results in relation to heart rate and ECG waveform morphology. For a texture that has shown 0% signal loss, the mean heart rate difference between the reference and experimental device was − 1.778 ± 4.654 Beats per minute (BPM); in terms of ECG waveform, the best cases present a Pearson correlation coefficient above 0.99.


1987 ◽  
Vol 33 (3) ◽  
pp. 749-754
Author(s):  
S.K. Dey
Keyword(s):  

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 111
Author(s):  
Pengjia Tu ◽  
Junhuai Li ◽  
Huaijun Wang ◽  
Ting Cao ◽  
Kan Wang

Human activity recognition (HAR) has vital applications in human–computer interaction, somatosensory games, and motion monitoring, etc. On the basis of the human motion accelerate sensor data, through a nonlinear analysis of the human motion time series, a novel method for HAR that is based on non-linear chaotic features is proposed in this paper. First, the C-C method and G-P algorithm are used to, respectively, compute the optimal delay time and embedding dimension. Additionally, a Reconstructed Phase Space (RPS) is formed while using time-delay embedding for the human accelerometer motion sensor data. Subsequently, a two-dimensional chaotic feature matrix is constructed, where the chaotic feature is composed of the correlation dimension and largest Lyapunov exponent (LLE) of attractor trajectory in the RPS. Next, the classification algorithms are used in order to classify and recognize the two different activity classes, i.e., basic and transitional activities. The experimental results show that the chaotic feature has a higher accuracy than traditional time and frequency domain features.


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