Sequence Of Body Movements Research Articles

CARIN

Recent studies have proposed to use the Channel State Information (CSI) of WiFi wireless channel for human gesture recognition. As an important application, CSI-based driver activity recognition in passenger vehicles has received increasing research attention. However, a serious limitation of almost all the existing WiFi-based recognition solutions is that they can only recognize the activity of a single person at a time, because the activities of other people (if performed at the same time) can interfere with the WiFi signals. In a sharp contrast, there can often be one or more passengers in any vehicles. In this paper, we propose CARIN, CSI-based driver Activity Recognition under the INterference of passengers. CARIN features a combination-based solution that profiles all the possible activity combinations of driver and (one or more) passengers in offline training and then performs recognition online. To attack possible combination explosion, we first leverage in-car pressure sensors to significantly reduce combinations, because there are only limited seating options in a passenger vehicle. We then formulate a distance minimization problem for fast runtime recognition. In addition, a period analysis methodology is designed based on the kNN classifier to recognize activities that have a sequence of body movements, like continuous head nodding due to driver fatigue. Our results in a real car with 3,000 real-world traces show that CARIN can achieve an overall F1 score of 90.9%, and outperforms the three state-of-the-art solutions by 32.2%.

Personalized Urination Activity Recognition Based on a Recurrent Neural Network Using Smart Band.

PurposeThough it is very important obtaining exact data about patients’ voiding patterns for managing voiding dysfunction, actual practice is very difficult and cumbersome. In this study, data about urination time and interval measured by smart band device on patients’ wrist were collected and analyzed to resolve the clinical arguments about the efficacy of voiding diary. By developing a smart band based algorithm for recognition of complex and serial pattern of motion, this study aimed to explore the feasibility of measurement the urination time and intervals for voiding dysfunction management.MethodsWe designed a device capable of recognizing urination time and intervals based on specific postures of the patient and consistent changes in posture. These motion data were obtained by a smart band worn on the wrist. An algorithm that recognizes the repetitive and common 3-step behavior for urination (forward movement, urination, backward movement) was devised based on the movement and tilt angle data collected from a 3-axis accelerometer. The sequence of body movements during voiding has consistent temporal characteristics, so we used a recurrent neural network and long short-term memory based framework to analyze the sequential data and to recognize urination time. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the value of the signals was calculated and then compared with the set analysis model to calculate the time of urination. A comparative study was conducted between real voiding and device-detected voiding to assess the performance of the proposed recognition technology.ResultsThe accuracy of the algorithm was calculated based on clinical guidelines established by urologists. The accuracy of this detecting device was high (up to 94.2%), proving the robustness of the proposed algorithm.ConclusionsThis urination behavior recognition technology showed high accuracy and could be applied in clinical settings to characterize patients’ voiding patterns. As wearable devices are developed and generalized, algorithms detecting consistent sequential body movement patterns reflecting specific physiologic behavior might be a new methodology for studying human physiologic behavior.

Social intuition as a form of implicit learning: Sequences of body movements are learned less explicitly than letter sequences

Social intuition as a form of implicit learning: Sequences of body movements are learned less explicitly than letter sequences

Social intuition as a form of implicit learning: Sequences of body movements are learned less explicitly than letter sequences.

In the current paper, we first evaluate the suitability of traditional serial reaction time (SRT) and artificial grammar learning (AGL) experiments for measuring implicit learning of social signals. We then report the results of a novel sequence learning task which combines aspects of the SRT and AGL paradigms to meet our suggested criteria for how implicit learning experiments can be adapted to increase their relevance to situations of social intuition. The sequences followed standard finite-state grammars. Sequence learning and consciousness of acquired knowledge were compared between 2 groups of 24 participants viewing either sequences of individually presented letters or sequences of body-posture pictures, which were described as series of yoga movements. Participants in both conditions showed above-chance classification accuracy, indicating that sequence learning had occurred in both stimulus conditions. This shows that sequence learning can still be found when learning procedures reflect the characteristics of social intuition. Rule awareness was measured using trial-by-trial evaluation of decision strategy (Dienes & Scott, 2005; Scott & Dienes, 2008). For letters, sequence classification was best on trials where participants reported responding on the basis of explicit rules or memory, indicating some explicit learning in this condition. For body-posture, classification was not above chance on these types of trial, but instead showed a trend to be best on those trials where participants reported that their responses were based on intuition, familiarity, or random choice, suggesting that learning was more implicit. Results therefore indicate that the use of traditional stimuli in research on sequence learning might underestimate the extent to which learning is implicit in domains such as social learning, contributing to ongoing debate about levels of conscious awareness in implicit learning.

What are you doing? What am I doing?

Psychology ought to describe how and explain why we human beings live our lives as we do, which necessarily comes down to how and why we engage in the actions and have the subjective experiencings that we do. Our physical actions are themselves in part subjective phenomena, because actions are not simply body movements but also essentially involve intentions, beliefs about specific causation, and a sense of voluntariness. Thus, whatever else it is, psychology is inescapably the science of explaining the personally subjective. It is time for psychology to openly embrace its subjective subject-matter as such, which would open up a vast domain still waiting to be systematically studied. The physical actions of persons are not simply self-proclaiming. They often require being skillfully discerned and interpreted and can often be made more or differently meaningful by a somewhat different discernment or interpretation, even sometimes to the actors themselves. This is the fundamental reason that body movement description is insufficient for providing the terms for scientific accounts of how we live and enact our lives, there being in general no satisfactorily objective way, once the nature of what physical actions really are is made clear, to establish just what action or actions have occurred in any sequence of body movements. Whether or not a finger was moved from one position to another, with what velocity it was moved, and similar physical facts may be objectively established, but such facts do not themselves indicate the possible actions -if anywhich thereby have occurred, any more than the mere shape of written words or an audiogram of speech conveys the linguistic meanings of (or the actions implicit in) any particular writing or speech. Such considerations as those of actor-perceived situational contexts and actor’s cultural customs, body movement capabilities, personal experiencings of wants, intentions, beliefs. . .are properly taken into account in the defining \\server05\productn\T\THE\25-2\THE203.txt unknown Seq: 2 13-JUL-06 12:28 258 Journal of Theoretical and Philosophical Psy. Vol. 25, No. 2, 2005 and interpretation of human actions. This can sometimes be a rather