Table 2 - uploaded by Francisco Nunes
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Total duration (minutes) and sample size (number of 10 seconds windows) for the 4 activity in- tensity levels.
Contexts in source publication
Context 1
... more than 41 hours of data were recorded for 14 activities divided into 4 activity intensity levels. The dataset was divided into train and test sets and details regarding the content of the dataset are provided in Table 2. ...
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Citations
... In addition, with the rapid development of mobile devices and mobile applications, objective data from wearables and smartphones have been used for studying shift workers. For example, Pereira et al. collected wearable accelerometer data from hospital shift workers and detected 4 levels of their physical activity intensity with an 83% accuracy score [31]. Feng et al. used wearable devices to collect physiological data from shift work nurses for ten weeks and applied a clustering method for extracting behavioral consistency, which intuitively captures unique behavioral patterns between different groups of nurses [7]. ...
Shift workers who are essential contributors to our society, face high risks of poor health and wellbeing. To help with their problems, we collected and analyzed physiological and behavioral wearable sensor data from shift working nurses and doctors, as well as their behavioral questionnaire data and their self-reported daily health and wellbeing labels, including alertness, happiness, energy, health, and stress. We found the similarities and differences between the responses of nurses and doctors. According to the differences in self-reported health and wellbeing labels between nurses and doctors, and the correlations among their labels, we proposed a job-role based multitask and multilabel deep learning model, where we modeled physiological and behavioral data for nurses and doctors simultaneously to predict participants' next day's multidimensional self-reported health and wellbeing status. Our model showed significantly better performances than baseline models and previous state-of-the-art models in the evaluations of binary/3-class classification and regression prediction tasks. We also found features related to heart rate, sleep, and work shift contributed to shift workers' health and wellbeing.
... The QREN AAL4ALL project (Ambient Assistive Living for All) developed an ecosystem of standardized and interoperable AAL products and services in Portugal, associated to a business model and validated through large scale trial, that involved over 400 elders across the country. In Clockwork project (Pereira & Nunes, 2018) the consortium implemented external synchronizers that will help users monitoring their daily living conditions and improve them. ...
We present an Environmental Analysis and Safety Advisor system capable of identify the environmental barriers and hazards found in the homes of elderly people. This augmented reality tool runs on a portable computing device and can be used by informal and formal caregivers without specific knowledge of Accessible Design, to evaluate the safeness of an elderly home environment, ensuring that potential fall hazards are detected and corrected. The system recognizes specific indoor elements of the house (e.g. armchair , bed, chair), and then computes and analyses their mutual distances in the environment so that a warning of hazard is emitted in case of need (e.g. loose cable, not enough space to pass a wheelchair). In this context, we implemented object recognition at the category level of miniature versions of real sized furniture and the determination of the distance between neighboring objects, signaling if it is below a certain threshold value. Environmental Analysis tool can then recognize furniture and measure the distance between two furniture elements enabling the system to pop up an alert sign if the space left does not guarantee good accessibility.
... These handicaps impact recognition accuracy [1] and may decrease user acceptance and adherence, motivating the development of a truly position-independent and automatic physical activity monitoring framework. Pervasive position-independent activity monitoring has explored the use of smartphones as the sensing device [2]- [10], and to less extent wearables [11]- [13]. However, these position-independent wearable solutions have not been released to the market nor have they been compared against market devices. ...
... • Smart Badge -a wearable device with an inertial measurement unit (IMU) and a light sensor, to track the activity and light exposure of shift workers when it is not practical to carry their smartphone (see [28,29]). Data is stored temporarily and transmitted to the smartphone with Bluetooth Low Energy (BLE); ...
... This paper focuses on the design of the Clockwork app due to its relevance in supporting shift workers in managing their shift work. Further detail about other parts of the system can be found at [28,29]. We opted for developing a smartphone application because smartphones are prevalent in many countries, can collect data using internal sensors, can display information, and provide internet connection. ...
... Besides running on the smartphone, the algorithm also runs on the Smart Badge, which enables users to gather data from their activity when using their smartphone is not practical or possible.16 The rational and design of the algorithm for classifying activity into a specific activity level have been reported elsewhere[29]. ...
Working in shifts can cause great disruption in a person's life, impacting their health, family, and social life. Previous work has raised numerous issues of shift work, but there is little understanding of how workers practically deal with the challenges of shift work. This study investigates how shift workers engage in self-care to avoid health issues and deal with their shifts in practical terms. Findings show that shift workers engage in numerous activities for preparing, managing, and recovering from night shifts. Moreover, we describe the design of a mobile app for shift workers, designed based on the characteristics and self-care practices of shift workers.
... The Smart Badge contains an accelerometer that implements a machine learning algorithm trained to classify physical activity intensity into four different levels: sedentary, light, moderate, and vigorous [16]. To make sense of these personal and environmental data, we developed an Android application that connects with the Smart Badge. ...
Wearable self-monitoring technologies can help users learn more about themselves. By enabling people to track their habits and visualising them, wearables can support self-awareness and self-care. For example, wearables can help shift workers dealing with unorthodox sleep schedules of shift work through tools that monitor their body and habits. However, current wearable technologies do not fit well in healthcare scenarios where workers engage in specific antiseptic practices. In this paper we present the process of designing the Smart Badge, a wearable for monitoring physical activity, sleep, and light exposure of hospital workers.
Lately, numerous new well-being Global Positioning System beacons and smart watches have shown up in a few worldwide electronic item shopper displays. The idea of wearable gadgets keeps on being hot, and the market request keeps on developing. As of now, the item types of wearable gadgets fundamentally incorporate brilliant glasses, smart watches, keen wristbands, and so on. By interfacing with the web-service and consolidating with different programming, we can furnish customers with some significant essential signs data and keep clients in contact. In any case, in down-to-earth clinical applications, wearable gadgets ought to guarantee the exactness of securing and guarantee comfort during the time spent employing. By lessening the volume of implantable devices, it improves their biological compatibility and perseverance. The solace insight of customer’s influences by the soundness of battery power gracefully, administration life, sensor position, power utilization, precision, and so forth. The proposal provides a summary of various contributions in the wearable devices domain. The contributions of various authors are analyzed and summarized.
Shift workers who are essential contributors to our society, face high risks of poor health and wellbeing. To help with their problems, we collected and analyzed physiological and behavioral wearable sensor data from shift working nurses and doctors, as well as their behavioral questionnaire data and their self-reported daily health and wellbeing labels, including alertness, happiness, energy, health, and stress. We found the similarities and differences between the responses of nurses and doctors. According to the differences in self-reported health and wellbeing labels between nurses and doctors, and the correlations among their labels, we proposed a job-role based multitask and multilabel deep learning model, where we modeled physiological and behavioral data for nurses and doctors simultaneously to predict participants’ next day’s multidimensional self-reported health and wellbeing status. Our model showed significantly better performances than baseline models and previous state-of-the-art models in the evaluations of binary/3-class classification and regression prediction tasks. We also found features related to heart rate, sleep, and work shift contributed to shift workers’ health and wellbeing.
