Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective DOI Creative Commons
Lai Wei, Stephen Jia Wang

JMIR mhealth and uhealth, Journal Year: 2024, Volume and Issue: 12, P. e46282 - e46282

Published: March 14, 2024

Background Motion tracking technologies serve as crucial links between physical activities and health care insights, facilitating data acquisition essential for analyzing intervening in activity. Yet, systematic methodologies evaluating motion data, especially concerning user activity recognition applications, remain underreported. Objective This study aims to systematically review daily living activities, emphasizing the critical interaction among devices, users, environments from a design perspective, analyze process involved application research. It intends delineate intricacies contexts, focusing on enhancing data’s accuracy applicability monitoring intervention strategies. Methods Using review, this research scrutinized their wellness, examining studies Scopus, Web of Science, EBSCO, PubMed databases. The used actor network theory data-enabled understand complex interplay humans, within these applications. Results Out 1501 initially identified studies, 54 (3.66%) were included in-depth analysis. These articles predominantly accelerometer gyroscope sensors (n=43, 80%) monitor motion, demonstrating strong preference capturing both dynamic static activities. While incorporating portable devices (n=11, 20%) multisensor configurations (n=16, 30%), across body (n=15, 28%) spaces (n=17, 31%) highlights diverse applications diversity reflects application’s alignment with types ranging movements specialized scenarios. results also reveal participant pool, including general public, athletes, groups, focus healthy individuals (n=31, 57%) athletes (n=14, 26%). Despite extensive range, primarily laboratory-based (n=39, 72%) aimed at professional uses, such precise identification joint functionality assessment, emphasizes significant challenge translating findings controlled conditions everyday Conclusions study’s comprehensive investigation technology reveals gap methods collection practical real-world proposes an innovative approach that includes designers process, importance framework. ensures is aligned varied nature Such integration developing are accessible, intuitive, tailored meet needs. By leveraging multidisciplinary combines design, engineering, sciences, opens new pathways usability effectiveness technologies.

Language: Английский

Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges DOI Creative Commons

Sofiat Abioye,

Lukumon O. Oyedele, Lukman Akanbi

et al.

Journal of Building Engineering, Journal Year: 2021, Volume and Issue: 44, P. 103299 - 103299

Published: Oct. 6, 2021

The growth of the construction industry is severely limited by myriad complex challenges it faces such as cost and time overruns, health safety, productivity labour shortages. Also, one least digitized industries in world, which has made difficult for to tackle problems currently faces. An advanced digital technology, Artificial Intelligence (AI), revolutionising manufacturing, retail, telecommunications. subfields AI machine learning, knowledge-based systems, computer vision, robotics optimisation have successfully been applied other achieve increased profitability, efficiency, safety security. While acknowledging benefits applications, numerous are relevant still exist industry. This study aims unravel examine techniques being used identify opportunites applications A critical review available literature on activity monitoring, risk management, resource waste was conducted. Furthermore, opportunities were identified presented this study. provides insights into key applies construction-specific challenges, well pathway realise acrueable

Language: Английский

Citations

552
Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review DOI Creative Commons

Massimo Regona,

Tan Yiğitcanlar, Bo Xia

et al.

Journal of Open Innovation Technology Market and Complexity, Journal Year: 2022, Volume and Issue: 8(1), P. 45 - 45

Published: March 1, 2022

Artificial intelligence (AI) is a powerful technology with range of capabilities, which are beginning to become apparent in all industries nowadays. The increased popularity AI the construction industry, however, rather limited comparison other industry sectors. Moreover, despite being hot topic built environment research, there review studies that investigate reasons for low-level adoption industry. This study aims reduce this gap by identifying challenges AI, along opportunities offered, To achieve aim, adopts systematic literature approach using PRISMA protocol. In addition, focuses on planning, design, and stages project lifecycle. results reveal (a) particularly beneficial planning stage as success projects depends accurate events, risks, cost forecasting; (b) major opportunity adopting time spent repetitive tasks big data analytics improving work processes; (c) biggest challenge incorporate site fragmented nature has resulted issues acquisition retention. findings inform parties operate concerning adaptability help increase market acceptance practices.

Language: Английский

Citations

285
Deep learning-based data analytics for safety in construction DOI

Jiajing Liu,

Hanbin Luo, Junxiao Liu

et al.

Automation in Construction, Journal Year: 2022, Volume and Issue: 140, P. 104302 - 104302

Published: May 10, 2022

Language: Английский

Citations

80
Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization DOI Creative Commons
Yassine Himeur, Somaya Al‐Maadeed, Hamza Kheddar

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 119, P. 105698 - 105698

Published: Dec. 16, 2022

Recently, developing automated video surveillance systems (VSSs) has become crucial to ensure the security and safety of population, especially during events involving large crowds, such as sporting events. While artificial intelligence (AI) smooths path computers think like humans, machine learning (ML) deep (DL) pave way more, even by adding training components. DL algorithms require data labeling high-performance effectively analyze understand recorded from fixed or mobile cameras installed in indoor outdoor environments. However, they might not perform expected, take much time training, have enough input generalize well. To that end, transfer (DTL) domain adaptation (DDA) recently been proposed promising solutions alleviate these issues. Typically, can (i) ease process, (ii) improve generalizability ML models, (iii) overcome scarcity problems transferring knowledge one another task another. Although increasing number articles develop DTL- DDA-based VSSs, a thorough review summarizes criticizes state-of-the-art is still missing. this paper introduces, best authors' knowledge, first overview existing shed light on their benefits, discuss challenges, highlight future perspectives.

Language: Английский

Citations

78
Artificial intelligence and sustainable development goals: Systematic literature review of the construction industry DOI Creative Commons

Massimo Regona,

Tan Yiğitcanlar, Carol K.H. Hon

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 108, P. 105499 - 105499

Published: May 4, 2024

While acknowledging the widespread recognition of artificial intelligence's (AI) potential in achieving sustainable development, there remains a notable deficiency and thorough examination its specific applications, impacts, challenges, particularly within construction industry. A comprehensive investigation is critical to explore understand multifaceted applications AI fostering sustainability across all phases project. This paper aims examine how can be effectively integrated key project phases—i.e., planning, design, construction, operation maintenance, through systematic literature review map their adoption best practices. The findings revealed: (a) Sustainable development goals (SDGs) pertinent industry—i.e., SDGs 6-9,11-13,15,17; (b) that show highest promote 7,9,11; (c) Within spectrum these goals, potentially transform industry contribute consideration processes more efficient resilient ways; (d) Ethical considerations, data privacy security concerns must addressed, along with an urgent need for specialised training maintenance systems; (e) Careful implementation management essential harness full potential, while addressing challenges sector.

Language: Английский

Citations

46
Deep-learning-based visual data analytics for smart construction management DOI
Aritra Pal, Shang‐Hsien Hsieh

Automation in Construction, Journal Year: 2021, Volume and Issue: 131, P. 103892 - 103892

Published: Aug. 19, 2021

Language: Английский

Citations

70
Digital technology for quality management in construction: A review and future research directions DOI Creative Commons
Hanbin Luo, Ling Lin, Ke Chen

et al.

Developments in the Built Environment, Journal Year: 2022, Volume and Issue: 12, P. 100087 - 100087

Published: Aug. 19, 2022

Significant developments in digital technologies can potentially provide managers and engineers with the ability to improve quality of construction industry. Acknowledging current future use management (CQM), we address following research question: What be used industry? In addressing this question, a systematic review approach is examine studies that have been for This indicates there need technology-based be: (1) enhance defect concealed work, (2) pre-construction defects prevention as well post-completion product function testing, (3) on compliance inspection direction. We suggest focus culture development, advanced data analytics, behavioral assessment.

Language: Английский

Citations

55
Automated vision-based construction progress monitoring in built environment through digital twin DOI Creative Commons
Aritra Pal, Jacob J. Lin, Shang‐Hsien Hsieh

et al.

Developments in the Built Environment, Journal Year: 2023, Volume and Issue: 16, P. 100247 - 100247

Published: Oct. 11, 2023

Effective progress monitoring is ineviTable for completing the construction of building and infrastructure projects successfully. In this digital transformation era, with data-centric management control approach, effectiveness methods expected to improve dramatically. "Digital Twin," which creates a bidirectional communication flow between physical entity its counterpart, found be crucial enabling technology information-aware decision-making systems in manufacturing other automotive industries. Recognizing benefits production construction, researchers have proposed Digital Twin Construction (DTC). DTC leverages information modeling processes, lean practices, on-site data collection mechanisms, Artificial Intelligence (AI) based analytics improving planning processes. Progress monitoring, key component control, can significantly benefit from DTC. However, some knowledge gaps still need filled practical implementation built environment domain. This research reviews existing vision-based methods, studies evolution automated research, highlights methodological technological that must addressed DTC-based predictive monitoring. Subsequently, it proposes framework closed-loop through Finally, way forward fully automated, real-time upon concept proposed.

Language: Английский

Citations

37
Real-time indoor localization with visual SLAM for in-building emergency response DOI

Po‐Yen Tseng,

Jacob J. Lin, Ying-Chieh Chan

et al.

Automation in Construction, Journal Year: 2022, Volume and Issue: 140, P. 104319 - 104319

Published: May 13, 2022

Language: Английский

Citations

32
Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation DOI

Anbang Peng,

Xiaoli Zhang, Wei Xu

et al.

Water Resources Management, Journal Year: 2022, Volume and Issue: 36(7), P. 2381 - 2394

Published: April 20, 2022

Language: Английский

Citations

27