Hae Young Noh

Hae Young Noh
Stanford University | SU · Department of Civil and Environmental Engineering

About

141
Publications
25,952
Reads
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2,399
Citations
Citations since 2017
101 Research Items
2240 Citations
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20172018201920202021202220230100200300400500
20172018201920202021202220230100200300400500

Publications

Publications (141)
Article
Full-text available
In this paper, we characterize the effects of obstructions on footstep-induced floor vibrations to enable obstruction-invariant indoor occupant localization. Occupant localization is important in smart building applications such as smart healthcare and energy management. Maintenance and installment requirements limit the application of current sens...
Article
Automated structural damage diagnosis after earthquakes is important for improving efficiency of disaster response and city rehabilitation. In conventional data-driven frameworks which use machine learning or statistical models, structural damage diagnosis models are often constructed using supervised learning. Supervised learning requires historic...
Chapter
In this paper, we characterize the effects of obstructions on footstep-induced floor vibrations to enable obstruction-invariant indoor occupant localization. Occupant localization is important in smart building applications such as healthcare and energy management. In prior works, footstep-based vibration sensing has been introduced for non-intrusi...
Article
Fine-grained city-scale outdoor air pollution maps provide important environmental information for both city managers and residents. Installing portable sensors on vehicles (e.g., taxis, Ubers) provides a low-cost, easy-maintenance, and high-coverage approach to collecting data for air pollution estimation. However, as non-dedicated platforms, vehi...
Article
Full-text available
Non-intrusive monitoring of fine-grained activities of daily living (ADL) enables various smart healthcare applications. For example, ADL pattern analysis for older adults at risk can be used to assess their loss of safety or independence. Prior work in the area of ADL recognition has focused on coarse-grained ADL recognition at the context-level (...
Article
Full-text available
A common pain point for physical retail stores is live inventory monitoring, i.e., knowing how many items of each product are left on the shelves. About 4% of sales are lost due to an average 5–10% out-of-shelf stockout rate, while additional supplies existed in the warehouse. Traditional techniques rely on manual inspection, per-item tagging using...
Data
This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set...
Conference Paper
Full-text available
We introduce a footstep-induced floor vibration sensing system that enables us to quantify the gait pattern of individuals with Muscular Dystrophy (MD) in non-clinical settings. MD is a neuromuscular disorder causing progressive loss of muscle, which leads to symptoms in gait patterns such as toe-walking, frequent falls, balance difficulty, etc. Ex...
Conference Paper
Full-text available
This paper introduces a window-based sequence-to-one approach with dynamic voting for nurse care activity recognition using acceleration-based wearable sensors. Nurse care activity recognition is an essential part of ensuring high quality patient care and providing constructive and concrete feedback to the care team. Some of the current sensing app...
Article
We introduce a Bayesian framework for incorporating time-varying noisy reported data on damage and loss information to update near real-time loss estimates/alerts for the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) system. Initial loss estimation by PAGER immediately following an earthquake includes several...
Article
Full-text available
Active learning (AL) methods create an optimized labeled training set from unlabeled data. We introduce a novel online active deep learning method for medical image analysis. We extend our MedAL AL framework to present new results in this paper. A novel sampling method queries the unlabeled examples that maximize the average distance to all trainin...
Article
A recent topic of considerable interest in the "smart building" community involves building interactive devices using sensors, and rapidly creating these objects using new fabrication methods. However, much of this work has been done at what might be called hand scale, with less attention paid to larger objects and structures (at furniture or room...
Article
Air pollution is a global health threat. Except static official air quality stations, mobile sensing systems are deployed for urban air pollution monitoring to achieve larger sensing coverage and greater sampling granularity. However, the data sparsity and irregularity also bring great challenges for pollution map recovery. To address these problem...
Preprint
Full-text available
(https://arxiv.org/abs/2006.03641) Monitoring bridge health using the vibrations of drive-by vehicles has various benefits, such as low cost and no need for direct installation or on-site maintenance of equipment on the bridge. However, many such approaches require labeled data from every bridge, which is expensive and time-consuming, if not impos...
Article
Full-text available
Fine-grained air pollution monitoring has attracted increasing attention worldwide. Even with an increasing amount of both static and mobile sensing systems, an inference algorithm is still essential to achieve a comprehensive understanding of the urban atmospheric environment. Conventional physical model-based methods are unable to involve all the...
Preprint
Full-text available
The resolution of GPS measurements, especially in urban areas, is insufficient for identifying a vehicle's lane. In this work, we develop a deep LSTM neural network model LaNet that determines the lane vehicles are on by periodically classifying accelerometer samples collected by vehicles as they drive in real time. Our key finding is that even adj...
Article
This paper presents a floor-vibration-based step-level occupant-detection approach that enables detection across different structures through model transfer. Detecting the occupants through detecting their footsteps (i.e., step-level occupant detection) is useful in various smart building applications such as senior/healthcare and energy management...
Preprint
Full-text available
Automated structural damage diagnosis after earthquakes is important for improving the efficiency of disaster response and rehabilitation. In conventional data-driven frameworks which use machine learning or statistical models, structural damage diagnosis models are often constructed using supervised learning. The supervised learning requires histo...
Article
Full-text available
We present a data-driven approach based on physical insights to achieve damage diagnosis of bridges using only vibration signals collected on board the vehicles passing over the bridge. Though data-driven models have been shown to produce promising results in this context, they generally require labeled examples to fit the models (i.e., supervised...
Article
Vehicular mobile crowdsensing (MCS) enables many smart city applications. Ride sharing vehicle fleets provide promising solutions to MCS due to advantages of low cost, easy maintenance, high mobility and long operational time. However, as non-dedicated mobile sensing platforms, the first priorities of these vehicles are delivering passengers, which...
Conference Paper
Full-text available
We propose a vehicle-vibration-based indirect structural health monitoring (SHM) framework that uses acceleration signals collected from within a moving vehicle to identify global modal and structural parameters of a full-scale and in-service bridge. Motivated by many benefits of indirect sensing methods, such as low-cost, low-maintenance and no in...
Conference Paper
Fine-grained non-intrusive monitoring of activities of daily living (ADL) enables various smart building applications, including ADL pattern assessments for older adults at risk for loss of safety or independence. Prior work in this area has focused on coarsegrained ADL recognition at the activity level (e.g., cooking, cleaning, sleeping), and/or c...
Conference Paper
Auto-checkout technology could revolutionize physical retail by bringing down operating costs and enabling automated stores. The first step towards fully autonomous stores is automated live inventory monitoring. Existing automated approaches tend to focus on vision only and are cost prohibitive, slow, or inaccurate for practical real-world applicat...
Conference Paper
Full-text available
A common pain point for physical retail stores is live inventory monitoring, i.e. knowing how many items of each product are left on the shelves. About 4% of sales are lost due to an average 5-10% out-of-shelf stockout rate, while additional supplies existed in the warehouse. Traditional techniques rely on manual inspection, per-item tagging using...
Conference Paper
Sensing signal quality affects signal processing efficiency, feature extraction, and learning accuracy. An efficient and accurate assessment of sensing system signal quality is essential for 1) large-scale cyber-physical system deployment and 2) datasets sharing and comparison. In this paper, we present a signal quality assessment -- S-score -- for...
Conference Paper
Structural vibration-based human sensing provides an alternative approach for device-free human monitoring, which is used for healthcare, space and energy usage management, etc. Prior work on this approach mainly focused on one person walking scenarios, which limits their widespread application. The challenge with multiple walkers is that the obser...
Conference Paper
Full-text available
Sleep disorder impairs people's health. To better analyze user's sleep quality, it is important to monitor people's sleep stages. Prior methods, such as polysomnography (PSG) and Fitbit, are often intrusive and having potential to change user's daily sleep routine. We present a non-intrusive approach to identify sleep stages through bed-frame vibra...
Article
A simple hardware array for obtaining highresolution structural vibration data will enable future civil infrastructures to become general sensing platforms that are easy and practical to deploy and maintain over the lifetime of a building.
Conference Paper
In this work, we propose P-Loc, a device-free indoor localization system based on power-line network within the building. P-Loc measures the electromagnetic (EM) coupling between a human body and existing power-lines, which are simultaneously used for electric power transmission. To avoid the impact of AC mains and noise from other electrical sourc...
Conference Paper
Air pollution is a global health threat. Nowadays, with the increasing amount of air pollution monitoring data from either conventional official stations or mobile sensing systems, the role of deep learning methods in pollution map recovery becomes gradually apparent. To address the challenges including the irregular sampling from mobile sensing an...
Preprint
Full-text available
Active Learning methods create an optimized and labeled training set from unlabeled data. We introduce a novel Online Active Deep Learning method for Medical Image Analysis. We extend our MedAL active learning framework to present new results in this paper. Experiments on three medical image datasets show that our novel online active learning model...
Article
Full-text available
We present DR-Train, the first long-term open-access dataset recording dynamic responses from in-service light rail vehicles. Specifically, the dataset contains measurements from multiple sensor channels mounted on two in-service light rail vehicles that run on a 42.2-km light rail network in the city of Pittsburgh, Pennsylvania. This dataset provi...
Conference Paper
With the growth of wearable devices, numerous health and smart building applications are enabled. As a result, many people wear multiple devices for different applications, such as fitness tracking. Being able to match devices' physical identity (e.g., smartwatch on Bob's left forearm) to their virtual identity (e.g., IP 192.168.0.22) is often a re...
Article
Full-text available
Sensor-based occupant tracking has the potential to enhance knowledge of the utilization of buildings. Occupancy-tracking strategies using footstep-induced floor vibrations may be beneficial for thermal-load prediction, security enhancement, and care-giving without undermining privacy. Current floor-vibration-based occupant-tracking methodologies a...
Article
Full-text available
Vehicular crowd sensing systems are designed to achieve large spatio-temporal sensing coverage with low-cost in deployment and maintenance. For example, taxi platforms can be utilized for sensing city-wide air quality. However, the goals of vehicle agents are often inconsistent with the goal of the crowdsourcer. This inconsistency decreases the sen...
Conference Paper
Full-text available
We propose a multi-task learning approach for estimating both the location and magnitude of damage occurring on an experimental bridge using acceleration signals collected from a passing vehicle. This is a low-cost and low-maintenance indirect structural health monitoring approach in which sensors on the vehicle are used to detect bridge damage. Re...
Conference Paper
Secure pairing is an important problem especially due to large number of IoT devices. In this paper, we propose PosePair++, to enable a camera to securely pair with IoT devices which are equipped with IMU sensors. Existing context-based pairing approaches do not adequately address this problem due to differing sensing modalities. To address this ch...
Conference Paper
Gait health monitoring is critical for condition diagnosis and fall prediction in elderly populations. Existing methods for gait health monitoring (e.g. direct observation and sensing) are not suitable for non-clinical environments due to qualitative assessments or operational limitations. Our method utilizes footstep-induced floor vibration sensin...
Conference Paper
Mobile crowd sensing (MCS) collects city-scale sensing data with low cost and high efficiency. One important goal of MCS is to ensure high quality of sensing coverage to provide sufficient information to data analysis end. However, the goal of the MCS may be inconsistent with the goal of vehicles. This inconsistency between goals results in a bad s...
Conference Paper
Full-text available
We present Deskbuddy, a vibration-based system that can track a user's activities through their desk. Tracking sitting and other office related activities let us remind the user to have healthier working habits, as well as giving information about how office spaces are used. Many solutions have been proposed for office activity tracking, but they e...
Conference Paper
Vehicular mobile crowdsensing (MCS) enables a lot of smart city applications, such as smart transportation, environmental monitoring etc. Taxis provide a good platform for MCS due to their long operational time and city-scale coverage. However, taxis, as a non-dedicated sensing platform, does not guarantee high sensing coverage quality (large and b...
Article
This paper presents an indoor area occupancy counting system utilizing the ambient structural vibration induced by pedestrian footsteps. Our system achieves 99.55% accuracy in pedestrian footsteps detection, 0.2 people mean estimation error in pedestrian traffic estimation, and 0.2 area occupant activity estimation error in real-world uncontrolled...
Article
Occupant identification proves crucial in many smart home applications such as automated home control and activity recognition. Previous solutions are limited in terms of deployment costs, identification accuracy, or usability. We propose SenseTribute, a novel occupant identification solution that makes use of existing and prevalent on-object senso...
Article
This paper presents a graphical approach to assess the detectability of multiple simultaneous faults in mechanical systems such as Air Handling Units (AHUs). Symptoms of multiple simultaneous faults can cancel each other out, resulting in no indication of abnormality and rendering these faults undetectable when the measurements collected from AHUs...
Article
Full-text available
Individual perspiration level indicates a person’s physical status as well as their comfort level. Therefore, continuous perspiration level measurement enables people to monitor these conditions for applications including fitness assessment, athlete physical status monitoring, and patient/elderly care. Prior work on perspiration (sweat) sensing req...
Conference Paper
Continuous heart rate monitoring in cars can allow ambient health monitoring and help track driver stress and fatigue. We present a data set from accelerometers embedded in a car seat which includes ten people sitting in the passenger seat of a moving car, and surface ECG data of each user to provide ground truth of the heartbeat. This data can be...
Conference Paper
Occupant activity level plays an important part in many smart building applications, such as power management [5] and elderly care [1]. There are several approaches to obtain occupant states, including visual-, acoustic-, and radio frequency based techniques. However, visual-based approaches require light-of-sight, while acoustic-based approaches a...
Conference Paper
Indoor localization systems cannot rely on the same mechanisms, like GPS, that are used for outdoor or large-scale localization. Instead, autonomous or user-carried devices are often localized by measuring the time taken for an emitted signal to reach a known location; this signal can be sound, light, radio waves, or another similar sensed quantity...
Conference Paper
Mobile sensing systems are deployed for urban air pollution monitoring to increase coverage over a city. However, the sampling irregularity brings great challenges for fine-grained pollution field recovery. To address this problem, we proposed a generative model based inference algorithm. By modeling the air pollution evolution and data sampling pr...
Conference Paper
Occupant activity level information is essential in many smart home applications, such as energy management and elderly care. Various methods have been proposed for detecting occupant activities through vision-, acoustic-, or radio frequency-based methods. However, the visual-based methods function only when occupants are in the visual field, the a...
Conference Paper
In this paper, we present a structure-adaptive approach for monitoring human gait using footstep-induced floor vibrations. Human gait information is critical for timely and accurate assessment and diagnosis of many health conditions. Footstep-induced vibration monitoring has been introduced in prior works as an accurate and cost-effective mean to p...
Conference Paper
Perspiration level monitoring enables numerous applications such as physical condition estimation, personal comfort monitoring, health/exercise monitoring, and inference of environmental conditions of the user. Prior works on perspiration (sweat) sensing require users to manually hold a device or attach adhesive sensors directly onto their skin, li...
Conference Paper
Large-scale fine-grained air pollution information has both financial for city managers and health benefits for all city residents. Sensors installed on fleet of vehicles (like taxis) to collect air pollution data provides a low cost, low-maintenance and a potentially high coverage approach. The challenges here are: 1) Sensing coverage is sparse in...
Data
This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America. In particular, two light rail vehicles were instrumented (identified...
Preprint
Full-text available
Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance.Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance. However, such large labeled...
Article
Modeling of the system behavior is a key step for better management and accurate fault detection and diagnosis of Air Handling Units (AHUs). This paper presents an extensive empirical investigation on a typical AHU. A data set from an active unit is analyzed using different existing forecasting techniques. Performances of these techniques are compa...
Preprint
Full-text available
Vehicular mobile crowd sensing is a fast-emerging paradigm to collect data about the environment by mounting sensors on vehicles such as taxis. An important problem in vehicular crowd sensing is to design payment mechanisms to incentivize drivers (agents) to collect data, with the overall goal of obtaining the maximum amount of data (across multipl...
Article
Swarms of Unmanned Aerial Vehicles (drones) could provide great benefit when used for disaster response and indoor search and rescue scenarios. In these harsh environments where GPS availability cannot be ensured, prior work often relies on cameras for control and localization. This creates the challenge of identifying each drone, i.e., finding the...