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The purpose of this paper is to develop a highly miniaturized wireless inertial sensor system based on a novel 3D packaging technique using a flexible printed circuit (FPC). The device is very suitable for wearable applications in which small size and lightweight are required such as body area network, medical, sports and entertainment applications.

Modern wireless inertial measurement units are typically implemented on a rigid 2D printed circuit board (PCB). The design concept presented here is based around the use of a novel planar, six‐faceted, crucifix or cross‐shaped FPC instead of a rigid PCB. A number of specific functional blocks (such as microelectromechanical systems gyroscope and accelerometer sensors, microcontroller (MCU), radio transceiver, antenna, etc.) are first assigned to each of the six faces which are each 1 cm² in area. The FPC cross is then developed into a 1 cm³, 3D configuration by folding the cross at each of five bend planes. The result is a low‐volume and lightweight, 1 cm³ wireless inertial sensor that can sense and send motion sensed data wirelessly to a base station. The wireless sensor device has been designed for low power operation both at the hardware and software levels. At the base station side, a radio receiver is connected to another MCU unit, which sends received data to a personal computer (PC) and graphical user interface. The industrial, scientific and medical band (2.45 GHz) is used to achieve half duplex communication between the two sides.

A complete wireless sensor system has been realized in a 3D cube form factor using an FPC. The packaging technique employed during the work is shown to be efficient in fabricating the final cubic system and resulted in a significant saving in the final size and weight of the system. A number of design issues are identified regarding the use of FPC for implementing the 3D structure and the chosen solutions are shown to be successful in dealing with these issues.

Currently, a limitation of the system is the need for an external battery to power the sensor system. A second phase of development would be required to investigate the possibility of the integration of a battery and charging system within the cube structure. In addition, the use of flexible substrate imposes a number of restrictions in terms of the ease of manufacturability of the final system due to the requirement of the required folding step.

The small size and weight of the developed system is found to be extremely useful in different deployments. It would be useful to further explore the system performance in different application scenarios such as wearable motion tracking applications. In terms of manufacturability, component placement needs to be carefully considered, ensuring that there is sufficient distance between the components, bend planes and board edges and this leads to a slightly reduced usable area on the printed circuit.

This paper provides a novel and useful method for realizing a wireless inertial sensor system in a 3D package. The value of the chosen approach is that a significant reduction in the required system volume is achieved. In particular, a 78.5 per cent saving in volume is obtained in decreasing the module size from a 25 to a 15 mm³ size.

The first step toward developing an automated construction workers performance monitoring system is to initially establish a complete and competent activity recognition solution, which is still lacking. This study aims to propose a novel approach of using labor physiological data collected through wearable sensors as means of remote and automatic activity recognition.

A pilot study is conducted against three pre-fabrication stone construction workers throughout three full working shifts to test the ability of automatically recognizing the type of activities they perform in-site through their lively measured physiological signals (i.e. blood volume pulse, respiration rate, heart rate, galvanic skin response and skin temperature). The physiological data are broadcasted from wearable sensors to a tablet application developed for this particular purpose, and are therefore used to train and assess the performance of various machine-learning classifiers.

A promising result of up to 88% accuracy level for activity recognition was achieved by using an artificial neural network classifier. Nonetheless, special care needs to be taken for some activities that evoke similar physiological patterns. It is expected that blending this method with other currently developed camera-based or kinetic-based methods would yield higher activity recognition accuracy levels.

The proposed method complements previously proposed labor tracking methods that focused on monitoring labor trajectories and postures, by using additional rich source of information from labors physiology, for real-time and remote activity recognition. Ultimately, this paves for an automated and comprehensive solution with which construction managers could monitor, control and collect rich real-time data about workers performance remotely.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

This chapter discusses the various ways in which audiovisual (AV) media industries have cooperated with the tourism industry and explores the emergent areas for cross-innovation. It demonstrates the gradual mediatisation of tourism, but also how the added value from location tourism has started to affect, for instance, the operation of the film industry. It then discusses the emergence of tourism gamification that came about with the arrival of smartphones equipped with an ever-increasing variety of sensors relevant to location and mobility awareness. The chapter finishes by discussing the affordances and forms of augmented reality being used in the service of the cultural heritage sector and the broader tourism sector.