Trends in Workplace Wearable Technologies and Connected‐Worker Solutions for Next‐Generation Occupational Safety, Health, and Productivity

Establishment-level safety analytics: a scoping review

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.

The application of metaverse in occupational health

Background

The metaverse, as a new digital interactive platform, is garnering significant attention and exploration across industries due to technological advancements and societal digital transformation. In occupational health, there is immense potential for leveraging the metaverse to enhance work environments and occupational health management. It offers companies more efficient and intelligent solutions for occupational health management while providing employees with safer and more comfortable work environments.

Methods

A comprehensive literature search was conducted using PubMed, Web of Science, IEEE Xplore, and Google Scholar databases to identify relevant studies published between January 2015 and March 2024. The search terms included "metaverse," "virtual reality," "occupational health," "workplace safety," "job training," and "telemedicine." The selected articles were analyzed, and key findings were summarized narratively.

Results

The review summarizes the broad application prospects of metaverse technology in immersive training, occupational risk identification and assessment, and occupational disease monitoring and diagnosis. However, applying the metaverse in occupational health also faces challenges such as inadequate technical standards, data privacy issues, human health hazards, high costs, personnel training, and lagging regulations.

Conclusion

Metaverse offers new possibilities for addressing the numerous challenges faced in occupational health and has broad application prospects. In the future, collaborative efforts from multiple stakeholders will be necessary to promote the sustainable development of metaverse technology in occupational health and better protect workers' occupational health.

Mixed-method usability investigation of ARROWS: augmented reality for roadway work zone safety

This article explores the usability and user experience challenges of ARROWS, a novel augmented reality (AR) and wearable technology (WT) safety system for roadway work zones, an area with limited existing usability research. We utilized a mixed-method approach with two complementary experiments in indoor and outdoor settings, using the Wizard of Oz methodology and a high-fidelity prototype. We focused on identifying usability challenges, factors contributing to user experience and the distinct needs of highway workers, documenting results using the system usability scale (SUS), the rating scale mental effort (RSME) and a trust score. Participants rated the usability of ARROWS above average in both settings, while making a reasonable level of mental effort. The findings also indicate a significant correlation between perceived trust and usability, highlighting the importance of trust in user experience.

Flexible and Stretchable Light-Emitting Diodes and Photodetectors for Human-Centric Optoelectronics

Optoelectronic devices with unconventional form factors, such as flexible and stretchable light-emitting or photoresponsive devices, are core elements for the next-generation human-centric optoelectronics. For instance, these deformable devices can be utilized as closely fitted wearable sensors to acquire precise biosignals that are subsequently uploaded to the cloud for immediate examination and diagnosis, and also can be used for vision systems for human-interactive robotics. Their inception was propelled by breakthroughs in novel optoelectronic material technologies and device blueprinting methodologies, endowing flexibility and mechanical resilience to conventional rigid optoelectronic devices. This paper reviews the advancements in such soft optoelectronic device technologies, honing in on various materials, manufacturing techniques, and device design strategies. We will first highlight the general approaches for flexible and stretchable device fabrication, including the appropriate material selection for the substrate, electrodes, and insulation layers. We will then focus on the materials for flexible and stretchable light-emitting diodes, their device integration strategies, and representative application examples. Next, we will move on to the materials for flexible and stretchable photodetectors, highlighting the state-of-the-art materials and device fabrication methods, followed by their representative application examples. At the end, a brief summary will be given, and the potential challenges for further development of functional devices will be discussed as a conclusion.

Reversible thermochromic fibers with excellent elasticity and hydrophobicity for wearable temperature sensors

Color-changing fibers, which can intuitively convey information to the human eye, can be used to facilely add functionality to various types of clothing. However, they are often expensive and complex, and can suffer from low durability. Therefore, in this study, we developed highly elastic and hydrophobic thermochromic fibers as wearable temperature sensors using a simple method that does not require an electric current. A thermochromic pigment was embedded inside and outside hydrophobic silica aerogel particles, following which the thermochromic aerogel was fixed to highly elastic spandex fibers using polydimethylsiloxane as a flexible binder. In particular, multi-strand spandex fibers were used instead of single strands, resulting in the thermochromic aerogels penetrating the inside of the strands upon their expansion by solvent swelling. During drying, the thermochromic aerogel adhered more tightly to the fibers by compressing the strands. The thermochromic fiber was purple at room temperature (25 °C), but exhibited a two-stage color change to blue and then white as the temperature increased to 37 °C. In addition, even after 100 cycles of tension-contraction at 200%, the thermochromic aerogel did not detach and was strongly attached to the fiber. Additionally, it was confirmed that color change due to temperature was stable even after exposure to 1 wt% NaCl (artificial sweat) and 0.1 wt% detergent solutions. The developed thermochromic fiber therefore exhibited excellent elasticity and hydrophobicity, and is expected to be widely utilized as an economical wearable temperature sensor as it does not require electrical devices.

Real-Time Monitoring of Underground Miners' Status Based on Mine IoT System

Real-time monitoring and timely risk warnings for the safety, health, and fatigue of underground miners are essential for establishing intelligent mines, enhancing the safety of production, and safeguarding the well-being of miners. This concerns the collection, transmission, and processing of relevant data. To minimize physical strain on miners, data collection functions are consolidated into two wearable terminals: an electronic bracelet equipped with reliable, low-power components for gathering vital sign data and transmitting them via Bluetooth and a miner lamp that integrates multi-gas detection, personnel positioning, and wireless communication capabilities. The gas sensors within the miner lamp undergo regular calibration to maintain accuracy, while the positioning tag supports round-trip polling to ensure a deviation of less than 0.3 m. Data transmission is facilitated through the co-deployment of 5G communication and UWB positioning base stations, with distributed MIMO networking to minimize frequent cell handovers and ensure a low latency of no more than 20 ms. In terms of data processing, a backpropagation mapping model was developed to estimate miners' fatigue, leveraging the strong correlation between saliva pH and fatigue, with vital signs as the input layer and saliva pH as the output layer. Furthermore, a unified visualization platform was established to facilitate the management of all miners' states and enable prompt emergency response. Through these optimizations, a monitoring system for underground miners' status based on mine IoT technology can be constructed, meeting the requirements of practical operations.

Exploring wearable technology use and importance of health monitoring in the hazardous occupations of first responders and professional drivers

OBJECTIVES

Hazardous materials (HAZMAT) pose risks to the health and safety of professionals involved with transportation and emergency responses. Two distinct occupational groups that encounter HAZMAT events are first responders and professional drivers. Wearable technology is a tool that can assist with monitoring the health of professionals involved in HAZMAT events. The aim of this study was to compare and evaluate the perceptions of first responders and professional drivers on wearable technology and attitudes toward health monitoring.

METHODS

A survey was administered to first responders (n = 112) and professional drivers (n = 218). Statistical approaches included bivariate analysis, latent class analysis, logistic regression analysis, and path analysis for the variables of interest.

RESULTS

There were significant differences between the groups in perceptions of the benefits of monitoring certain health indicators. Professional drivers were more likely to have a history of wearable technology use compared with first responders (odds ratio [OR] = 10.1; 95% CI, 4.42-22.9), reported greater exposure to HAZMAT (OR = 4.32; 95% CI, 2.24-8.32), and were more willing to have their health data monitored by someone other than themselves (OR = 9.27; 95% CI, 3.67-23.4). A multinomial regression model revealed that occupation was not a significant predictor of class preference for acceptance of monitoring specific health indicators.

CONCLUSIONS

Occupation appeared to be important but further analysis uncovered that characteristics of individuals within the occupations were more salient to the use of wearable technology. HAZMAT exposure, someone else monitoring health data, and experience with wearable technology use were found to be important factors for perceptions about benefits of health monitoring with wearable technology.

Influence of an upper limb exoskeleton on muscle activity during various construction and manufacturing tasks

Musculoskeletal disorders (MSDs) significantly impact workers in the manufacturing and construction sectors. One solution that has gained interest to reduce MSDs incidence is the use of exoskeletons. In this study, the influence of an upper limb exoskeleton on muscle activity was investigated experimentally for three commonly performed tasks in the manufacturing and construction sectors. The tasks tested were overhead assembly, bricklaying, and box moving tasks. Eighteen males participated in the tests. The results showed a reduction in shoulder flexor muscle activation during all three tasks (up to -45.46 ± 4.52% for the anterior deltoid), but increased extensor activation (up to 15.47 ± 8.01% for the latissimus dorsi) was observed when the task was not primarily performed above shoulder level. The results revealed the dependence of the upper-body exoskeleton on tasks and arm posture, which should be considered for both in-field applications and designing new exoskeletons for performance enhancement.

Wearable Nano-Based Gas Sensors for Environmental Monitoring and Encountered Challenges in Optimization

With a rising emphasis on public safety and quality of life, there is an urgent need to ensure optimal air quality, both indoors and outdoors. Detecting toxic gaseous compounds plays a pivotal role in shaping our sustainable future. This review aims to elucidate the advancements in smart wearable (nano)sensors for monitoring harmful gaseous pollutants, such as ammonia (NH3), nitric oxide (NO), nitrous oxide (N2O), nitrogen dioxide (NO2), carbon monoxide (CO), carbon dioxide (CO2), hydrogen sulfide (H2S), sulfur dioxide (SO2), ozone (O3), hydrocarbons (CxHy), and hydrogen fluoride (HF). Differentiating this review from its predecessors, we shed light on the challenges faced in enhancing sensor performance and offer a deep dive into the evolution of sensing materials, wearable substrates, electrodes, and types of sensors. Noteworthy materials for robust detection systems encompass 2D nanostructures, carbon nanomaterials, conducting polymers, nanohybrids, and metal oxide semiconductors. A dedicated section dissects the significance of circuit integration, miniaturization, real-time sensing, repeatability, reusability, power efficiency, gas-sensitive material deposition, selectivity, sensitivity, stability, and response/recovery time, pinpointing gaps in the current knowledge and offering avenues for further research. To conclude, we provide insights and suggestions for the prospective trajectory of smart wearable nanosensors in addressing the extant challenges.

Application of Intelligent Lie Recognition Technology in Laws and Regulations Based on Occupational Mental Health Protection

INTRODUCTION

Since the reform and opening up, the social economy has developed rapidly. The competition in the employer market is fierce, which leads leaders to have strict requirements for workers, and workplace stress increases. The blind pursuit of corporate economic benefits has led to the neglect of workers' mental health. Employee retaliation against the corporate occurs frequently. The perfection of the legal system for occupational mental health protection is imminent.

METHODS

Based on the above questions, this study first introduces the research background, significance, and purpose in the introduction. Second, in the literature review, the current status of research is sorted out, the problems in the existing research are summarized, and the innovation points of this study are highlighted. Then, in the method section, the algorithms and models used here are introduced, including convolutional neural networks, long short-term memory networks, and the design of interview processes. Finally, the results of the questionnaire survey and the experimental test are analyzed.

RESULTS

(1) There is further room for optimization of intelligent lie recognition technology. (2) The employee assistance program system can effectively solve the mental health problems of employees. (3) There is a need to expand the legislative mechanism for workers' mental health protection at the legal level.

DISCUSSION

This study mainly explores the loopholes of occupational mental health protection under the formulation of laws and regulations. Intelligent lie recognition technology reduces workers' adverse physical and mental health risks due to work. It is dedicated to protecting workers' legitimate rights and interests from the formulation of laws and regulations.

Vibrotactile Alerting to Prevent Accidents in Highway Construction Work Zones: An Exploratory Study

Struck-by accidents are the leading cause of injuries in highway construction work zones. Despite numerous safety interventions, injury rates remain high. As workers' exposure to traffic is sometimes unavoidable, providing warnings can be an effective way to prevent imminent threats. Such warnings should consider work zone conditions that can hinder the timely perception of alerts, e.g., poor visibility and high noise level. This study proposes a vibrotactile system integrated into workers' conventional personal protective equipment (PPE), i.e., safety vests. Three experiments were conducted to assess the feasibility of using vibrotactile signals to warn workers in highway environments, the perception and performance of vibrotactile signals at different body locations, and the usability of various warning strategies. The results revealed vibrotactile signals had a 43.6% faster reaction time than audio signals, and the perceived intensity and urgency levels on the sternum, shoulders, and upper back were significantly higher than the waist. Among different notification strategies used, providing a moving direction imposed significantly lower mental workloads and higher usability scores than providing a hazard direction. Further research should be conducted to reveal factors that affect alerting strategy preference towards a customizable system to elicit higher usability among users.

Wrist-worn sensor-based measurements for drug effect detection with small samples in people with Lewy Body Dementia

INTRODUCTION

Few late-stage clinical trials in Parkinson's disease (PD) have produced evidence on the clinical validity of sensor-based digital measurements of daily life activities to detect responses to treatment. The objective of this study was to assess whether digital measures from patients with mild-to-moderate Lewy Body Dementia demonstrate treatment effects during a randomized Phase 2 trial.

METHODS

Substudy within a 12-week trial of mevidalen (placebo vs 10, 30, or 75 mg), where 70/344 patients (comparable to the overall population) wore a wrist-worn multi-sensor device.

RESULTS

Treatment effects were statistically significant by conventional clinical assessments (Movement Disorder Society-Unified Parkinson's Disease Rating Scale [MDS-UPDRS] sum of Parts I-III and Alzheimer's Disease Cooperative Study-Clinical Global Impression of Change [ADCS-CGIC] scores) in the full study cohort at Week 12, but not in the substudy. However, digital measurements detected significant effects in the substudy cohort at week 6, persisting to week 12.

CONCLUSIONS

Digital measurements detected treatment effects in a smaller cohort over a shorter period than conventional clinical assessments.

TRIAL REGISTRATION

clinicaltrials.gov, NCT03305809.

How Accurately Can Wearable Sensors Assess Low Back Disorder Risks during Material Handling? Exploring the Fundamental Capabilities and Limitations of Different Sensor Signals

Low back disorders (LBDs) are a leading occupational health issue. Wearable sensors, such as inertial measurement units (IMUs) and/or pressure insoles, could automate and enhance the ergonomic assessment of LBD risks during material handling. However, much remains unknown about which sensor signals to use and how accurately sensors can estimate injury risk. The objective of this study was to address two open questions: (1) How accurately can we estimate LBD risk when combining trunk motion and under-the-foot force data (simulating a trunk IMU and pressure insoles used together)? (2) How much greater is this risk assessment accuracy than using only trunk motion (simulating a trunk IMU alone)? We developed a data-driven simulation using randomized lifting tasks, machine learning algorithms, and a validated ergonomic assessment tool. We found that trunk motion-based estimates of LBD risk were not strongly correlated (r range: 0.20-0.56) with ground truth LBD risk, but adding under-the-foot force data yielded strongly correlated LBD risk estimates (r range: 0.93-0.98). These results raise questions about the adequacy of a single IMU for LBD risk assessment during material handling but suggest that combining an IMU on the trunk and pressure insoles with trained algorithms may be able to accurately assess risks.

Algorithms and the future of work

An algorithm refers to a series of stepwise instructions used by a machine to perform a mathematical operation. In 1955, the term artificial intelligence (AI) was coined to indicate that a machine could be programmed to duplicate human intelligence. Even though that goal has not yet been reached, the use of sophisticated machine learning algorithms has moved us closer to that goal. While algorithm-enabled systems and devices will bring many benefits to occupational safety and health, this Commentary focuses on new sources of worker risk that algorithms present in the use of worker management systems, advanced sensor technologies, and robotic devices. A new "digital Taylorism" may erode worker autonomy, and lead to work intensification and psychosocial stress. The presence of large amounts of information on workers within algorithmic-enabled systems presents security and privacy risks. Reliance on indiscriminate data mining may reproduce forms of discrimination and lead to inequalities in hiring, retention, and termination. Workers interfacing with robots may face work intensification and job displacement, while injury in the course of employment by a robotic device is also possible. Algorithm governance strategies are discussed such as risk management practices, national and international laws and regulations, and emerging legal accountability proposals. Determining if an algorithm is safe for workplace use is rapidly becoming a challenge for manufacturers, programmers, employers, workers, and occupational safety and health practitioners. To achieve the benefits that algorithm-enabled systems and devices promise in the future of work, now is the time to study how to effectively manage their risks.

Validation of Wearable Device Consisting of a Smart Shirt with Built-In Bioelectrodes and a Wireless Transmitter for Heart Rate Monitoring in Light to Moderate Physical Work

Real-time monitoring of heart rate is useful for monitoring workers. Wearable heart rate monitors worn on the upper body are less susceptible to artefacts caused by arm and wrist movements than popular wristband-type sensors using the photoplethysmography method. Therefore, they are considered suitable for stable and accurate measurement for various movements. In this study, we conducted an experiment to verify the accuracy of our developed and commercially available wearable heart rate monitor consisting of a smart shirt with bioelectrodes and a transmitter, assuming a real-world work environment with physical loads. An exercise protocol was designed to light to moderate intensity according to international standards because no standard exercise protocol for the validation simulating these works has been reported. This protocol includes worker-specific movements such as applying external vibration and lifting and lowering loads. In the experiment, we simultaneously measured the instantaneous heart rate with the above wearable device and a Holter monitor as a reference to evaluate mean absolute percentage error (MAPE). The MAPE was 0.92% or less for all exercise protocols conducted. This value indicates that the accuracy of the wearable device is high enough for use in real-world cases of physical load in light to moderate intensity tasks such as those in our experimental protocol. In addition, the experimental protocol and measurement data devised in this study can be used as a benchmark for other wearable heart rate monitors for use for similar purposes.

Systematic Development of the ReWin Application: A Digital Therapeutic Rehabilitation Innovation for People With Stroke-related Disabilities in India

This is a viewpoint paper that aims to describe the systematic approach to the development of a technology-driven stroke rehabilitation innovation to manage disabilities following a stroke at home in India. This paper intends to sensitize public health innovators and intervention development experts about the important aspects that need to be considered to develop a culturally sensitive, patient-centered, scalable solution for stroke care using technology. Stroke has been the second-leading cause of death and the third-leading cause of disability globally for the past 3 decades. The emerging technological innovations for stroke care were predominantly designed and developed by digital technology experts as stand-alone products with very minimal efforts to explore their feasibility, acceptability, and, more importantly, scalability. Hence, a digital therapeutic rehabilitation innovation for people with stroke-related disabilities in India was systematically developed and is being evaluated. ReWin is an innovation that is technologically driven and envisions digital therapeutics as a medium for the provision of rehabilitation to persons with disabilities. It is conceptualized and developed based on the International Classification of Functioning, Disability and Health. ReWin encompasses specific technological aspects to enable its scientific framework and conceptualization to suit the context and needs of stroke care providers and consumers. The framework is built with 2 separate applications, one for the providers and one for the patients and caregivers. Each of these applications has a specific inbuilt design to add data about the demographic details of the user, stroke severity using the National Institute of Health Stroke Scale, and self-assessment of disability measured by the modified Barthel Index. Users can communicate with each other and decide on their therapeutic goals, therapy training information, and progress remotely from where they are. The ultimate outcome expected from the ReWin innovation is a continuum of care for stroke survivors that is effective, safe, and of good quality. Systematic development cannot make the intervention scalable. The intervention needs to be evaluated for its feasibility, acceptability, and effectiveness. Currently, ReWin is being evaluated for its feasibility and acceptability. The evaluation of ReWin will provide an opportunity to develop a scalable solution for empowering therapists and persons with disabilities, in general, to objectively self-manage their treatment. Findings from this study will also provide valuable information about the resources required to deliver such interventions in resource-constrained settings like India.

Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review

BACKGROUND

As a form of the Internet of Things (IoT)-gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety.

OBJECTIVE

This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors?

METHODS

We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86%; EBSCO: 149/1268, 11.75%; ScienceDirect: 248/1268, 19.55%; and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility.

RESULTS

Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting.

CONCLUSIONS

A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality.

Fabrication of hydrophobic PLA filaments for additive manufacturing

There is an ever-greater need for self-cleaning and water-repelling properties of hydrophobic materials at this time in history, mainly due to the coronavirus disease 2019 (COVID-19) pandemic. However, the fabrication processes used to create hydrophobic materials are typically time-consuming and costly. Thus, this study aims to create hydrophobic materials based on low-cost manufacturing. In this study, polylactic acid (PLA) was mixed with various concentrations of hexadecyltrimethoxysilane (HDTMS) and polytetrafluoroethylene (PTFE) with the aid of solvents, chloroform, and acetone, through the solvent casting and melt extrusion process, which is capable of producing hydrophobic PLA filaments suitable for additive manufacturing (AM). Water contact angle (WCA) measurements were performed to verify the improved hydrophobicity of PLA/HDTMS/PTFE filaments. According to the results, it was discovered that the best filament WCAs were achieved with 2 g (10 wt%) of PLA, 0.2 ml of HDTMS, and 1 ml of PTFE (2 g PLA + 0.2 ml HDTMS + 1 ml PTFE), producing an average WCA of 131.6° and the highest WCA of 132.7°. These results indicate that adding HDTMS and PTFE to PLA significantly enhances filament hydrophobicity. Additionally, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA) techniques were utilized to characterize the surface morphology, molecular interactions, and thermal decompositions of the prepared PLA/HDTMS/PTFE filaments. This study revealed that compared to 2 g of pure PLA filament, HDTMS and PTFE altered the microstructure of the filament. Its thermal degradation temperature was impacted, but the melting temperature was not. Therefore, the PLA/HDTMS/PTFE filament is good enough to be printed by the fused filament fabrication (FFF) AM process.

An Energy-Autonomous Smart Shirt Employing Wearable Sensors for Users’ Safety and Protection in Hazardous Workplaces

Wearable devices represent a versatile technology in the IoT paradigm, enabling non-invasive and accurate data collection directly from the human body. This paper describes the development of a smart shirt to monitor working conditions in particularly dangerous workplaces. The wearable device integrates a wide set of sensors to locally acquire the user’s vital signs (e.g., heart rate, blood oxygenation, and temperature) and environmental parameters (e.g., the concentration of dangerous gas species and oxygen level). Electrochemical gas-monitoring modules were designed and integrated into the garment for acquiring the concentrations of CO, O2, CH2O, and H2S. The acquired data are wirelessly sent to a cloud platform (IBM Cloud), where they are displayed, processed, and stored. A mobile application was deployed to gather data from the wearable devices and forward them toward the cloud application, enabling the system to operate in areas where a WiFi hotspot is not available. Additionally, the smart shirt comprises a multisource harvesting section to scavenge energy from light, body heat, and limb movements. Indeed, the wearable device integrates several harvesters (thin-film solar panels, thermoelectric generators (TEGs), and piezoelectric transducers), a low-power conditioning section, and a 380 mAh LiPo battery to accumulate the recovered charge. Field tests indicated that the harvesting section could provide up to 216 mW mean power, fully covering the power requirements (P¯ = 1.86 mW) of the sensing, processing, and communication sections in all considered conditions (3.54 mW in the worst-case scenario). However, the 380 mAh LiPo battery guarantees about a 16-day lifetime in the complete absence of energy contributions from the harvesting section.