Research

Ongoing Projects

Non-intrusive Elderly Smart-home Healthcare System for Monitoring Short and Long term Anomaly in Daily Activity Patterns

In response to the rapidly aging population, this research aims to construct a smart home monitoring platform for elderly people’s activities of daily living to improve their health. The proposed platform will 1) continuously monitor daily activities of the elderly; 2) evaluate their activities of daily living; 3) identify the occurrence of an emergent situation (including accidents) and the decline in physical and cognitive functions. To achieve this goal, this research 1) propose an algorithm—reflecting the preference of non-invasive (Non-intrusive Sensing)—that can identify daily living activities using fine-grained Wi-Fi and energy use monitoring, through this, 2) develop a model that can analyze the abnormal activities that can occur in everyday life and its degree
.


Acknowledgment: This project is supported by the Korea Agency for Infrastructure Technology Advancement #17CTAP-C128499-01.



Human-Centric Sensing Platform to Assess Neighborhood Physical Disorder

Our vision is to create a human-centric sensing framework that identifies and locates neighborhoods’ built environments’ physical disorders in semi real-time. Assuming human movement is analogous to thermodynamics (e.g., changes in the entropy of particles are caused by additional heat), this framework will detect changes in the collective entropy of humans’ physiological responses (i.e., Gait Patterns, Heart Rate, Galvanic Skin Response) to discover the presence of physical disorder elements. This project will encompass two novel components: (1) the design of a novel collective response entropy (CRE) model that evaluates community residents’ physiological sensing data; and (2) the creation of a participatory sensing platform that diagnoses the physical disorders of a community in semi real-time. This framework effectively connects a physical system with cyber space, a linkage that will yield unprecedented progress in built-environment assessment practices.

Acknowledgment: This project is supported by the National Science Foundation Award #1538029 and #1800310




Revealing Hidden Safety Hazards Using Workers' Collective Bodily and Behavioral Response Patterns

The objective of this research is to examine whether, how, and to what extent workers' collective bodily and behavioral response patterns identify recognized/unrecognized hazards for the purpose of enhancing safety performance in construction environments. This research focuses on detecting hazards that causes fall accidents, a single most dangerous injury event within the construction industry, using workers - kinematic sensing data captured from wearable inertial measurement sensors. This research hypothesizes that the collective abnormalities apparent in multiple workers' balance and gait in one location is correlated with the likelihood of the presence (and/or the risk) of a recognized/unrecognized fall hazard in that location.

Acknowledgment: This project is supported by the National Science Foundation Award #1538029 and #1800310.







Past Projects
Occupant Intervention System to Drive Energy Efficiency in Commercial and Institutional buildings
Occupant intervention, which aims to change the energy consumption 
behavior of building occupants, has the potential to achieve significant energy savings in building operations using a cost-effective manner. However, its implementation in commercial and institutional buildings is particularly challenging due to the fact that it is difficult to track the energy consumption of part-time/temporary occupants and the fact that there is a complicated relationship between energy users and utility bill payers. The goal of this research is thus to develop a system to track temporary occupants’ energy load and provide effective occupant intervention in commercial and institutional building settings.


Acknowledgment: This project is supported by the UNL Research Council Interdisciplinary Grant.






Developing a Crack Routing Device for Improving the Current Crack Preparation Practices

Crack routing allows the crack to be cleaned properly, exposes a clean side wall for better adhesion, and allows sealing material in the crack expand and contract during hot and cold climates. Conventional crack routing methods are largely ineffective, labor intensive and/or dangerous. The main objective of this project is to upgrade the design of the pneumatic crack cleaning device, which was previously developed by the research team, particularly for a crack routing function and to evaluate its effectiveness for improving the current crack preparation practices and for possible adoption as a standard in NDOR.

Acknowledgment: This project is supported by Nebraska Department of Roads.




Construction Safety Hazard Detection Through Continuous Monitoring of Construction Workers’ Behavior Using Wearable Wireless Sensor Networks

The dynamic construction environment and unpredictable workers’ behaviors pose a key challenge in the identification of safety hazards within a construction jobsite. The goal of this research is thus to provide a continuous and reliable assessment of safety hazards, in particular related to fall accidents, by exploring the application of low-cost wireless sensor networks (WSNs), consisting of wearable inertial measurement units (IMU) and wireless data communication devices, in monitoring construction workers' behaviors.

Acknowledgment: This project is supported by the Nebraska Research Initiative(NRI).





Recognizing Activities of Construction Equipment using a Smartphone

The overall objective of the proposed research is to test a framework that automatically recognizes the driving and work events of heavy diesel equipment, using a smartphone as a multi-sensor hub, which allows assessing the productivity of equipment-related operation, quantifying air pollutant emissions generated from equipment use, and identifying operators’ driving patterns as related to safety. This research, in particular, focuses on the analysis of data from inertial measurement units (IMU) (e.g., three-axis accelerometer, gyroscope, magnetometer) embedded in a smartphone, and leverages the effectiveness of the driving event recognition by data fusion with other sensing information from the smartphone (e.g., GPS, cellular data network, video cameras).





An Integrated Framework for Estimating, Benchmarking and Monitoring the Environmental Impacts of Construction Operations

Construction operations are highly energy-intensive and account for significant environmental impacts, including Greenhouse Gases (GHG) and other engine exhaust emissions. This research aims to establish an integrated management framework that encompasses environmentally conscious planning and environmental performance monitoring in order to facilitate environmentally sustainable construction project.


  

   Related Publications


  1. Lee, H., Ahn, C. R., Choi, N., Kim, T., and Lee, H. (2019) “The Effects of Housing Environments on the Performance of Activity-Recognition Systems Using WiFi Channel State Information: An Exploratory Study.” Sensors, MDPI. (Accepted)
  2. Lee, B., Lee, H., Park, M., Ahn, C. R., Choi, N., and Kim, T. (2019). “Using Multiple Sequence Alignment to Extract Daily Activity Routines of the Elderly Living Alone”, Advanced in Computational Design: Advances in Computational Modelling and Data Analytics for Civil and Building Engineering. (accepted)
  3. Lee, H., Park, M., Ahn, C. R. (2019). “Quantifying the Variability of a Living-alone Elderly’s Activities of Daily Living: Clustering Analysis of Spatio-temporal Occupancy Data”, The CIB World Building Congress 2019, 17 - 21 June, 2019, Hong Kong, China.
  4. Lee, H., Ahn, C. R., Choi, N., Kim, T. (2019). "Toward Practical Implementation of Wi-Fi-based Activity Recognition in Smart Home." The 2019 ASCE International Conference on Computing in Civil Engineering, ASCE, June 17-19, 2019, Atlanta, Georgia, GA
  5. Lee, H., Ahn, C. R., Choi, N. (2018). “Frequency-Domain Analysis for Wi-Fi Based Human Activity Recognition Systems in Smart Homes.” The 18th International Conference on Construction Apllication of Virtual Reality, November 22-23, 2018, Auckland, New Zealand.  
  6. Lee, B., Lee, H., Park, M., Ahn, C. R., Choi, N., and Kim, T. (2018). “Extracting Routines of the Living Alone Occupant’s Daily Activities Using Multiple Sequence Alignment”, 17th International Conference on Computing in Civil and Building Engineering, June 5-7, 2018, Tampere, Finland.
  7. Lee, H., Sharma A., Ahn, C. R., Choi, N., and Kim, T. (2018) “Effects of Housing Environments on Exploiting Channel State Information for Human Activity Recognition.” 17th International Conference on Computing in Civil and Building Engineering, June 5-7, 2018, Tampere, Finland.


   Related Publications

  1. Kim, J., Ahn, C. R., Nam, Y. (2019) “The Influence of Built Environment Features on Crowdsourced Physiological Responses of Pedestrians in Neighborhoods Computers, Environment and Urban System, Elsevier, 75, 161-169.
  2. Kim, H., Ahn, C.R., and Yang, K. (2016)."A People-Centric Sensing Approach to Detecting Sidewalk Defects.Advanced Engineering Informatics, Elsevier, Vol. 30, No. 4, pp. 660-671.
  3. Kim, J., Yadav, M., Ahn, C. R., Chaspari, T. (2019). "Saliency Detection Analysis of Pedestrians' Physiological Responses to Assess Adverse Built Environment Features." The 2019 ASCE International Conference on Computing in Civil Engineering, ASCE, June 17-19, 2019, Atlanta, Georgia, GA
  4. Yadav, M., Chaspari, T., Kim, J., Ahn, C. R. (2018). "Capturing and quantifying emotional distress in the built environment.Proceedings of the Workshop on Human-Habitat for Health (H3): Human-Habitat Multimodal Interaction for Promoting Health and Well-Being in the Internet of Things Era (p. 9). ACM, October 16, 2018, Boulder, CO.
  5. Bisadi, M., Kim, H., Ahn, C.R., and Nam, Y. (2017). “Effects of Physical Disorders in Neighborhoods on Pedestrians’ Physiological Responses.” ASCE International Workshop on Computing in Civil Engineering, ASCE, June 25-27, 2017, Seattle, WA.
  6. Kim, H., Yang, K., and Ahn, C.R. (2016) “Feasibility of Using Pedestrians’ Physical Stability to Detect Defects in a Sidewalk.” 16th International Conference on Computing in Civil and Building Engineering, ISCCBE, July 6-8, 2016, Osaka, Japan



   Related Publications


  1. Yang, K. and Ahn C.R. (2019) “Inferring Workplace Safety Hazards from the Spatial Patterns of Workers’ Wearable Data” Advanced Engineering Informatics, Elsevier. (in review) 
  2. Sun, C., Ahn, S., and Ahn, C.R. (2019) “Can we identify workers insensitive to safety risks by sensing their behavior around hazards?” Journal of Construction Engineering and Management, ASCE. (in review)
  3. Ahn, C. R., Lee, S., Sun, C., Jebelli, H., Yang, K., Choi, B. (2019) “Review of Wearable Sensing Technology Applications in Construction Safety and Health” Journal of Construction Engineering and Management, ASCE. (in review)
  4. Yang, K., Ahn, C. R., & Kim, H. (2019). "Validating ambulatory gait assessment technique for hazard sensing in construction environments.Automation in Construction, Elsevier, 98, 302-309.
  5. Kim, H., Ahn, C. R., Stentz, T. L., & Jebelli, H. (2018). "Assessing the effects of slippery steel beam coatings to ironworkers' gait stability.Applied ergonomics68, 72-79.
  6. Yang, K., Ahn, C.R., Vuran, C. M., and Kim, H. (2017)."Collective Sensing of Workers' Gait Patterns to Identify Fall Hazards in Construction.Automation in Construction, Elsevier, Vol. 82, pp. 166-178.
  7. Yang, K., Ahn, C.R., Vuran, C. M., and Aria, S. (2016). “Semi-Supervised Near-Miss Fall Detection for Ironworkers with a Wearable Inertial Measurement Unit.”Automation in Construction, Elsevier, Vol. 68, pp. 194 - 202.
  8. Jebelli, H., Ahn, C. R., and Stentz, T. (2016). "Fall Risk Analysis of Construction Workers Using Inertial Measurement Units: Validating the Usefulness of the Postural Stability Metrics in Construction.Safety Science, Elsevier, Vol. 84, pp. 161 - 170.
  9. Kim, H., Ahn, C.R., and Yang, K. (2016). "Identifying Safety Hazards Using Collective Bodily Response of Workers." Journal of Construction Engineering and Management10.1061/(ASCE)CO.1943-7862.0001220 , 04016090. (Selected as Editor's Choice; Top 10 Most Read Paper)
  10. Jebelli, H., Ahn, C. R., and Stentz, T. L. (2015) "Comprehensive Fall-Risk Assessment of Construction Workers Using Inertial Measurement Units: Validation of the Gait-Stability Metric to Assess the Fall Risk of Iron Workers.Journal of Computing in Civil Engineering, 10.1061/(ASCE) CP.1943-5487.0000511, 04015034.
  11. Yang, K., Ahn, C.R., and Kim, H. (2018). “Tracking Divergence in Workers’ Trajectory Patterns for Hazard Sensing in Construction.” Construction Research Congress 2018. ASCE, April 2-4, 2018, New Orleans, LA
  12. Sun, C., Ahn, C.R., Bae, J., Johnson, M. (2018) “Monitoring Changes in Gait Adaptation to Identify Construction Workers’ Risk Preparedness after Multiple Exposures to a Hazard” Construction Research Congress 2018. ASCE, April 2-4, 2018, New Orleans, LA
  13. Jebelli, H., Yang, K., Khalili, M., Ahn, C.R., Stentz, T. L. (2018) “Assessing the Effects of Tool-Loading Formation on Construction Workers’ Postural Stability.” Construction Research Congress 2018. ASCE, April 2-4, 2018, New Orleans, LA
  14. Sun, C. , Ahn, C.R., Yang, K., Stentz, T., and Kim, H. (2017). “Deciphering Workers’ Safety Attitudes by Sensing Gait Patterns.” 19th International Conference on Human-Computer Interaction, July 9-14, Vancouver, Canada (Invited paper).
  15. Sun, C., Ahn, S., and Ahn, C.R.(2017). “Investigating the Impact of Human Risk Taking Tendency on the Likelihood of Struck-by Accidents in Construction using Agent-Based Simulation.” ASCE International Workshop on Computing in Civil Engineering, ASCE, June 25-27, 2017, Seattle, WA.
  16. Yang, K., Ahn, C.R., Vuran, C. M., and Kim, H. (2017). “Analyzing Spatial Patterns of Workers' Gait Cycles for Locating Latent Fall Hazards.” ASCE International Workshop on Computing in Civil Engineering, ASCE, June 25-27, 2017, Seattle, WA.
  17. Yang, K., Ahn, C.R., Vuran, C. M., and Kim, H. (2016). “Sensing Workers Gait Abnormality for Safety Hazard Identification.” Proceedings of the 33rd International Symposium on Automation and Robotics in Construction (ISARC), July 18-21, 2016, Auburn, AL.
  18. Yang, K., Kim, H., Ahn, C.R. and Stentz. T. (2016) “A Near-Miss Fall Detection Technique for Ironworkers Using a Hybrid Machine Learning Approach.” 16th International Conference on Computing in Civil and Building Engineering, ISCCBE, July 6-8, 2016, Osaka, Japan
  19. Jebelli, H., Yang, K., Ahn, C.R., and Stentz. T. (2015). “Symmetrical and Asymmetrical Tool Belt Loading Effects on the Postural Stability of Construction Workers.” 5th International/11th Construction Specialty Conference, CSCE, June 8-10, 2015, Vancouver, BC.
  20. Yang, K. , Jebelli, H., Ahn, C.R., Vuran, C. M. (2015). “Threshold-Based Approach to Detect Near-Miss Falls of Iron-Workers Using Inertial Measurement Units.ASCE International Workshop on Computing in Civil Engineering, ASCE, June 21-23, 2015, Austin, TX.
  21. Aria, S., Yang, K., Ahn, C.R., Vuran, C. M. (2014). “Near-Miss Accident Detection for Ironworkers Using Inertial Measurement Unit Sensors.” 31st International Symposium on Automation and Robotics in Construction and Mining (ISARC2014), July 9-11, 2014, Sydney, Australia. 
  22. Jebelli, H., Ahn, C.R., and Stentz. T. (2014). “The Validation of Gait-Stability Metrics to Assess Construction Workers’ Fall Risk.” 2014 International Conference for Computing in Civil and Building Engineering (ICCCBE), Orlando, FL, June 23-25, 2014.
  23. Yang, K., Aria, S., Ahn, C.R., and Stentz, T. (2014). “Automated Detection of Near-miss Fall Incidents in Iron Workers using Inertial Measurement Units.Construction Research Congress 2014, Atlanta, GA, May 19-21, 2014.