Pametne uredske stolice sa senzorima za otkrivanje položaja i navika sjedenja – pregled literature

The health consequences of prolonged sitting in the office and other work chairs have recently been tried to be alleviated or prevented by the application of modern technologies. Smart technologies and sensors are installed in different parts of office chairs, which enables monitoring of seating patterns and prevents positions that potentially endanger the health of users. The aim of this paper is to provide an overview of previous research in the field of the application of smart technologies and sensors built into office and other types of chairs in order to prevent diseases. The articles published in the period 2010-2020 and indexed in WoS CC, Scopus, and IEEE Xplore databases, with the keywords “smart chair” and “sensor chair” were analysed. 15 articles were processed, with their research being based on the use of different types of sensors that determine the contact pressures between the user’s body and stool parts and recognise different body positions when sitting, which can prevent negative health consequences. Analysed papers prove that the use of smart technology and a better understanding of sitting, using various sensors and applications that read body pressure and determine the current body position, can act as preventive health care by detecting proper heart rate and beats per minute, the activity of individual muscle groups, proper breathing and estimates of blood oxygen levels. In the future research, it is necessary to compare different types of sensors, methods used and the results obtained in order to determine which of them are most suitable for the future development of seating furniture for work.Posljedice dugotrajnog sjedenja na uredskim i drugim radnim stolicama u posljednje se vrijeme pokušavaju ublažiti ili spriječiti primjenom suvremenih tehnologija. U različite dijelove uredskih stolica ugrađuju se pametne tehnologije i senzori, što omogućuje praćenje rasporeda sjedenja i izbjegavanje položaja koji potencijalno ugrožavaju zdravlje korisnika. Cilj ovog rada jest davanje pregleda dosadašnjih istraživanja u području primjene suvremenih pametnih tehnologija i senzora ugrađenih u uredske i ostale vrste stolica radi prevencije obolijevanja korisnika. Analizirani su članci objavljeni u razdoblju od 2010. do 2020. i indeksirani su u bazama podataka WoS CC, Scopus i IEEE Xplore, a izdvojeni su prema ključnim riječima pametna stolica i senzorska stolica. Obrađeno je 15 članaka u kojima su se istraživanja temeljila na primjeni različitih vrsta senzora koji određuju kontaktne tlakove između korisnikova tijela i dijelova stolice te raspoznaju različite položaje tijela pri sjedenju, čime se mogu prevenirati negativne posljedice za zdravlje. U analiziranim istraživanjima autori su dokazali da primjena pametne tehnologije i bolje razumijevanje sjedenja uporabom različitih senzora i aplikacija kojima se očitava pritisak tijela i određuje njegov trenutačni položaj može preventivno djelovati zahvaljujući praćenju rada srca i broja otkucaja u minuti, aktivnosti pojedinih mišićnih skupina, pravilnog disanja, procjene razine kisika u krvi i sl. U budućim istraživanjima potrebno je usporediti različite tipove senzora, primijenjene metode i dobivene rezultate kako bi se uočilo koji su od njih najprikladniji za budući razvoj radnog namještaja za sjedenje

Context Mining of Sedentary Behavior for Promoting Self-Awareness Using Smartphone

Sedentary behaviour is increasing due to societal changes and is related to prolonged periods of sitting. There is sufficient evidence proving that sedentary behaviour has a negative impact on people’s health and wellness. This paper presents our research findings on how to mine the temporal contexts of sedentary behaviour by utilizing the on-board sensors of a smartphone. We use the accelerometer sensor of the smartphone to recognize user situations (i.e., still or active). If our model confirms that the user context is still, then there is a high probability of being sedentary. Then, we process the environmental sound to recognize the micro-context, such as working on a computer or watching television during leisure time. Our goal is to reduce sedentary behaviour by suggesting preventive interventions to take short breaks during prolonged sitting to be more active. We achieve this goal by providing the visualization to the user, who wants to monitor his/her sedentary behaviour to reduce unhealthy routines for self-management purposes. The main contribution of this paper is two-fold: (i) an initial implementation of the proposed framework supporting real-time context identification; (ii) testing and evaluation of the framework, which suggest that our application is capable of substantially reducing sedentary behaviour and assisting users to be active

Learning Outcomes With Gamification: An Integrative Review of Gamification in Training For Occupational Health Psychology

Gamification has received a lot of recognition over the past few years. The current generation has grown up playing video games and online gaming is increasing daily. Thus, I/O psychologists and other scholars have focused their attention on gamification in order to build intrinsic motivation in employees. Gamification can be explained as a method of applying gaming techniques in non-gaming concepts in order to increase productivity, knowledge, motivation, etc. Due to the increase in technology a different approach to encourage learning is crucial. The current generation of workers have been brought up using games and thus, using gamification at work places has better chances of increasing positive worker behavior. This paper summarizes the literature on gamification used in work places to improve the physical health of workers. This paper focuses on studies that use either gamification or non-gamification techniques in order to differentiate between the physical activity of the employees

Context mining of sedentary behaviour for promoting self-awareness using a smartphone

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Sedentary behaviour is increasing due to societal changes and is related to prolonged periods of sitting. There is sufficient evidence proving that sedentary behaviour has a negative impact on people’s health and wellness. This paper presents our research findings on how to mine the temporal contexts of sedentary behaviour by utilizing the on-board sensors of a smartphone. We use the accelerometer sensor of the smartphone to recognize user situations (i.e., still or active). If our model confirms that the user context is still, then there is a high probability of being sedentary. Then, we process the environmental sound to recognize the micro-context, such as working on a computer or watching television during leisure time. Our goal is to reduce sedentary behaviour by suggesting preventive interventions to take short breaks during prolonged sitting to be more active. We achieve this goal by providing the visualization to the user, who wants to monitor his/her sedentary behaviour to reduce unhealthy routines for self-management purposes. The main contribution of this paper is two-fold: (i) an initial implementation of the proposed framework supporting real-time context identification; (ii) testing and evaluation of the framework, which suggest that our application is capable of substantially reducing sedentary behaviour and assisting users to be active

Advancing the measurement of sedentary behaviour : classifying posture and physical (in-)activity

Sedentary behaviour, defined by a sitting body posture with minimal-intensity physical activity, is an emergent public health topic. The time spent sedentary is associated with the incidence of non-communicable chronic diseases such as type 2 diabetes and cardiovascular disease and significantly shortens life-expectancy in a dose-response relationship. Office workers are at particular risk of developing diseases related to sedentary behaviour due to their excessive sedentary work. Even though thigh-worn posture sensors are recommended to measure sedentary behaviour, the vast majority of the evidence was collected with waist-worn physical activity sensors, and we still lack a method to measure the posture and the physical activity component of sedentary behaviour simultaneously. This thesis aims to advance the measurement of sedentary behaviour in an office context by developing new device-based methods to measure both components simultaneously, and by validating and subsequently applying the most promising method to measure the actual amount of sedentary behaviour in the daily life of office workers. The method development showed that it is possible to measure both components of sedentary behaviour with only one sensor, preferably worn on the thigh or waist. While an accelerometer is sufficient for the thigh, an inertial-measurement-unit is preferable for the waist due to a significantly improved posture classification. The method validation subsequently confirmed that waist-worn physical activity sensors, the prevailing choice to measure sedentary behaviour, measure minimal-intensity physical activity. Furthermore, the study uncovered a serious postural dependency causing a systematic overestimation of minimal-intensity physical activity while sitting compared to standing. The subsequent method application considered the posture dependency and combined a thigh-worn posture sensor with a waist-worn physical activity sensor to POPAI, the Posture and Physical Activity Index. POPAI has a sensitivity of 92.5% and a specificity of 91.9% to measure sedentary behaviour and classified 45.0% of the office workers wake-time sedentary. The posture sensor alone overestimated sedentary time by 30.3%, and the physical activity sensor alone overestimated sedentary time by 22.5%. The difference can be explained by active sitting (2.0 hours per day) and inactive standing (1.8 hours per day), both of which are much more common than previously thought. This thesis confirms the recommendation to use a thigh-worn accelerometer to measure sedentary behaviour and adds the information that such a sensor is also able to measure physical (in-)activity in sitting. Thus, there is no need to approximate sedentary behaviour with sitting, nor is there a need to approximate it with inactivity. In fact, these approximations lead to inaccurate and imprecise results substantially overestimating sedentary behaviour. Due to the predominant use of physical activity sensors to measure sedentary behaviour, recommendations to limit sedentary behaviour should address a limitation of the time spent inactive rather than the time spent sitting. If it turns out that sitting matters, one could expect a much stronger relationship between sedentary behaviour measured with a combined method such as POPAI and detrimental health effects

Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step

Increase in workload across many organisations and consequent increase in occupational stress is negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of self- reporting and variability between and within individuals. With the advent of smartphones it is now possible to monitor diverse aspects of human behaviour, including objectively measured behaviour related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behaviour that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (in comparison to location, video or audio recording, for example) and because its low power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. 30 subjects from two different organizations were provided with smartphones. The study lasted for 8 weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify self reported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.Comment: in IEEE Journal of Biomedical and Health Informatics, 201

Wearables, smartphones, and artificial intelligence for digital phenotyping and health

Ubiquitous progress in wearable sensing and mobile computing technologies, alongside growing diversity in sensor modalities, has created new pathways for the collection of health and well-being data outside of laboratory settings, in a longitudinal fashion. Wearable and mobile devices have the potential to provide low-cost, objective measures of physical activity, clinically relevant data for patient assessment, and scalable behavior monitoring in large populations. These data can be used in both interventional and observational studies to derive insights regarding the links between behavior, health. and disease, as well as to advance the personalization and effectiveness of commercial wellness applications. Today, over 400,000 participants have had their behavior tracked prospectively using accelerometers for epidemiological studies across the globe. Traditionally, epidemiologists and clinicians have relied upon self-report measures of physical activity and sleep which, while valuable in the absence of alternatives, are subject to bias and often provide partial, incomplete information Physical behavior data extracted from wearable devices are being used to derive sensor-assessed, objective measures of physical behaviors, overcoming the limitations of self-report with the aim of relating these to clinical endpoints and eventually applying the findings to preventive and predictive medicine. Moreover, the application of artificial intelligence (AI), sensor fusion, and signal processing to wearable sensor data has led to improved human activity recognition and behavioral phenotyping. Here, we review the state of the art in wearable and mobile sensing technology in epidemiology and clinical medicine and discuss how AI is changing the field