Healthcare of an organization: Using wearable sensors and feedback system for energizing workers

Ara,; Akitomi,; Satô,; Tsuji,; Hayakawa,; Wakisaka,; Ohkubo,; Otsuka,; Beniyama,; Moriwaki,; Yano,

doi:10.1109/aspdac.2011.5722254

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…It should be noted here that other factors can cause body movements, in addition to face-to-face communication such as conversation. For instance, it is reported that body movement frequencies during face-to-face communication depend on the type of communication, but do not vary much from subject to subject [22] . Therefore, consider the situation of subjects i and j being in an area where they are mutually detectable by their wearable sensors, but subject i is communicating with subject m and subject j is communicating with subject n .…”

Section: Methodsmentioning

confidence: 99%

Interpersonal Similarity between Body Movements in Face-To-Face Communication in Daily Life

et al. 2014

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Individuals are embedded in social networks in which they communicate with others in their daily lives. Because smooth face-to-face communication is the key to maintaining these networks, measuring the smoothness of such communication is an important issue. One indicator of smoothness is the similarity of the body movements of the two individuals concerned. A typical example noted in experimental environments is the interpersonal synchronization of body movements such as nods and gestures during smooth face-to-face communication. It should therefore be possible to estimate quantitatively the smoothness of face-to-face communication in social networks through measurement of the synchronization of body movements. However, this is difficult because social networks, which differ from disciplined experimental environments, are open environments for the face-to-face communication between two individuals. In such open environments, their body movements become complicated by various external factors and may follow unstable and nonuniform patterns. Nevertheless, we consider there to be some interaction during face-to-face communication that leads to the interpersonal synchronization of body movements, which can be seen through the interpersonal similarity of body movements. The present study aims to clarify such interaction in terms of body movements during daily face-to-face communication in real organizations of more than 100 people. We analyzed data on the frequency of body movement for each individual during face-to-face communication, as measured by a wearable sensor, and evaluated the degree of interpersonal similarity of body movements between two individuals as their frequency difference. Furthermore, we generated uncorrelated data by resampling the data gathered and compared these two data sets statistically to distinguish the effects of actual face-to-face communication from those of the activities accompanying the communication. Our results confirm an interpersonal similarity of body movements between two individuals in face-to-face communication, for all the organizations studied, and suggest that some body interaction is behind this similarity.

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Section: Methodsmentioning

confidence: 99%

Interpersonal Similarity between Body Movements in Face-To-Face Communication in Daily Life

et al. 2014

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“…Begloe et al proposed a way to extract behavioral patterns for both individuals and teams of workers through the analysis of online presence data [4]. Ara et al proposed a system that can understand human communication processes in an office using wearable sensors [2]. Aral et al examined the effects of the diffusion patterns on the productivity and performance of information workers using accounting data for project co-work relationships and ten months of email traffic.…”

Section: Analysis Of Office Workersmentioning

confidence: 99%

Analysis of the correlation between the regularity of work behavior and stress indices based on longitudinal behavioral data

Okada

Sato

Kamiya

et al. 2012

Proceedings of the 14th ACM International Conference on Multimodal Interaction

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Increasingly, longitudinal behavioral data captured by various sensors are being analyzed to improve workplace performance. In this paper, we analyze the correlation between the regularity of workers' behavior and their levels of stress. We used a 23-month behavioral dataset for 18 workers that recorded their use of PCs and their locations in the office. We found that the principal eigenbehaviors extracted from the dataset with PCA represented typical work behaviors such as overwork using a PC and routine times for meetings. We found that more than 80% of each of the 18 workers' individual behaviors could be reconstructed using nine principal eigenbehaviors. In addition, the deviation ranges for the reconstruction accuracies were significantly different for workers in different positions. We conducted the correlation analysis between work behaviors of the workers and their stress level. Our results show a significant negative correlation (r ≥ 0.69, p < 0.01) between the accuracy of reconstructed work behaviors and physical stress levels; and a significant positive correlation between the accuracy of reconstructed behavior and stress dissolution abilities. Our results suggest that the correlation between the stress level of workers and the regularity of their work behavior exists. This correlation will be useful for occupational healthcare.

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“…In the past few years, various studies have explored the use of artificial intelligence, data mining, machine learning, and Internet of Things for various HR purposes, such as candidate selection, employee mood and sentiment analysis, and churn prediction. Different methods have been used to this end: correlating job requirements with individual résumés (Bollinger, Hardtke, & Martin, ; Yi, Allan, & Croft, ); analysing candidate video clips (such as provided by HireVue) and identifying characteristics or qualities incompatible with the job; predicting eventual and actual employee attritions by using prediction algorithms and social media data (Punnoose & Ajit, ; Robinson, Sinar, & Winter, ); identifying employee moods and emotions such as happiness, surprise, anger, disgust, fear, and sadness, by analysing facial expressions captured by the organization's cameras (facial emotion detection; Subhashini & Niveditha, ); analysing voice tones being used (Chan & Eric, ); analysing sentiments through online employee reviews (Moniz & Jong, ) and social media platforms (Costa & Veloso, ); and inspecting employee productivity by sensors installed on employee badges (Ara et al, ). Such sensors enable identifying movement, tone of voice, speech speed, employee cohesion, and so forth; exploring the effect of social media use on employee performance and motivation (Leftheriotis & Giannakos, ); and measuring employee knowledge sharing by analysing information shared in social media (van Zoonen, Verhoeven, & Vliegenthart, ) or in organizational intranets (Koriat & Gelbard, ; Koriat & Gelbard, ).…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis in organizational work: Towards an ontology of people analytics

et al. 2018

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The present paper proposes a conceptual ontology to evaluate human factors by modelling their key performance indicators and defining these indicators' explanatory factors, manifestations, and diverse corresponding digital footprints. Our methodology incorporates 6 main human resource constructs: performance, engagement, leadership, workplace dynamics, organizational developmental support, and learning and knowledge creation. Using sentiment analysis, we introduce a potential way to evaluate several components of the proposed human factors ontology. We use the Enron email corpus as a test case, to demonstrate how digital footprints can predict such phenomena. In so doing, we hope to encourage further research applying data mining techniques to allow real‐time, less costly, and more reliable assessments of human factor patterns and trends.

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Healthcare of an organization: Using wearable sensors and feedback system for energizing workers

Cited by 8 publications

References 6 publications

Interpersonal Similarity between Body Movements in Face-To-Face Communication in Daily Life

Interpersonal Similarity between Body Movements in Face-To-Face Communication in Daily Life

Analysis of the correlation between the regularity of work behavior and stress indices based on longitudinal behavioral data

Sentiment analysis in organizational work: Towards an ontology of people analytics

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