An experimental approach for the characterization of prolonged sitting postures using pressure sensitive mats

The adoption of prolonged sitting posture,which is a condition commonly encountered in several working tasks,is known to induce a wide range of negative effects,including discomfort,which has been recognized as an early predictor for musculoskeletal disorders (particularly low back pain).In this regard,the continuous monitoring of worker’s psychophysical state while sitting for long periods of time, may result useful in to preventing and managing potentially risky situations and to promote ergonomics and macroergonomics interventions,aimed to better organize work shifts and workplaces.The aim of this dissertation is to provide and test the reliability of a set of monitoring parameters,based on the use of quantitative information derived from body-seat contact pressure sensors.In particular, he study was focused on the assessment of trunk postural sway (the small oscillations resulting from the stabilization control system) and the number of In Chair Movements (ICM) or postural shifts performed while sitting, proven as a reliable tool for discomfort prediction. This thesis is articulated into four experimental campaigns.The first is a pilot study which aimed to define the most reliable algorithm and the set of parameters useful to assess the performed postural shifts or In chair Movements (ICM), which result useful to characterize postural strategies in the long term-monitoring. In this regard, a pilot study was conducted in which two different algorithms for the ICM computing were tested, based on different parameters and having different thresholds. The chosen algorithm was used, together with trunk sway parameters, to evaluate postural strategies in the other three experiments of this thesis. The second and the third studies evaluated sitting postural strategies among bus drivers during regular, long-term work shifts performed on urban and extra-urban routes. The results, in this case, showed that, all drivers reported a constant increase in perceived discomfort levels and a correspondent increase in trunk sway and overall number of ICM performed. This may indicate the adoption of specific strategies in order to cope with discomfort onset, a fatigue-induced alteration of postural features, or both simultaneously. However, it was interesting to observe differences in ICM vs trunk sway trend considering the single point-to-point route in the case of urban drivers. This difference between may indicate that these parameters refer to different aspects of sitting postural strategies: ICM may be more related to discomfort while sway may be more representative of task-induced fatigue. Trunk sway monitoring, as well as the count of ICM performed by bus drivers may thus be a useful tool in detecting postural behaviors potentially associated with deteriorating performance and onset of discomfort. Finally, the last experiment aimed to characterize modifications in sitting behavior, in terms of trunk sway and ICM among office workers during actual shifts. Surprisingly, results showed a decreasing trend in trunk sway parameters and ICM performed over time, with significant modifications in sitting posture in terms of trunk flexion-extension. Subjects were also stratified basing on their working behavior (staying seated or making short breaks during the trial) and significant differences were identified among these two groups in terms of postural sway and perceived discomfort. This may indicate that the adoption of specific working strategies can significantly influence sitting behavior and discomfort onset. In conclusion, the trunk sway monitoring and the ICM assessment in actual working environments may represent a useful tool to detect specific postural behaviors potentially associated with deteriorating performance and onset of discomfort, both among professional drivers and office workers.They might effectively support the evaluation of specific working strategies,as well as the set-up of macroergonomics interventions

Detection of Driver Drowsiness and Distraction Using Computer Vision and Machine Learning Approaches

Drowsiness and distracted driving are leading factor in most car crashes and near-crashes. This research study explores and investigates the applications of both conventional computer vision and deep learning approaches for the detection of drowsiness and distraction in drivers. In the first part of this MPhil research study conventional computer vision approaches was studied to develop a robust drowsiness and distraction system based on yawning detection, head pose detection and eye blinking detection. These algorithms were implemented by using existing human crafted features. Experiments were performed for the detection and classification with small image datasets to evaluate and measure the performance of system. It was observed that the use of human crafted features together with a robust classifier such as SVM gives better performance in comparison to previous approaches. Though, the results were satisfactorily, there are many drawbacks and challenges associated with conventional computer vision approaches, such as definition and extraction of human crafted features, thus making these conventional algorithms to be subjective in nature and less adaptive in practice. In contrast, deep learning approaches automates the feature selection process and can be trained to learn the most discriminative features without any input from human. In the second half of this research study, the use of deep learning approaches for the detection of distracted driving was investigated. It was observed that one of the advantages of the applied methodology and technique for distraction detection includes and illustrates the contribution of CNN enhancement to a better pattern recognition accuracy and its ability to learn features from various regions of a human body simultaneously. The comparison of the performance of four convolutional deep net architectures (AlexNet, ResNet, MobileNet and NASNet) was carried out, investigated triplet training and explored the impact of combining a support vector classifier (SVC) with a trained deep net. The images used in our experiments with the deep nets are from the State Farm Distracted Driver Detection dataset hosted on Kaggle, each of which captures the entire body of a driver. The best results were obtained with the NASNet trained using triplet loss and combined with an SVC. It was observed that one of the advantages of deep learning approaches are their ability to learn discriminative features from various regions of a human body simultaneously. The ability has enabled deep learning approaches to reach accuracy at human level.

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio