Research on HAR-Based Floor Positioning

Floor positioning is an important aspect of indoor positioning technology, which is closely related to location-based services (LBSs). Currently, floor positioning technologies are mainly based on radio signals and barometric pressure. The former are impacted by the multipath effect, rely on infrastructure support, and are limited by different spatial structures. For the latter, the air pressure changes with the temperature and humidity, the deployment cost of the reference station is high, and different terminal models need to be calibrated in advance. In view of these issues, here, we propose a novel floor positioning method based on human activity recognition (HAR), using smartphone built-in sensor data to classify pedestrian activities. We obtain the degree of the floor change according to the activity category of every step and determine whether the pedestrian completes floor switching through condition and threshold analysis. Then, we combine the previous floor or the high-precision initial floor with the floor change degree to calculate the pedestrians’ real-time floor position. A multi-floor office building was chosen as the experimental site and verified through the process of alternating multiple types of activities. The results show that the pedestrian floor position change recognition and location accuracy of this method were as high as 100%, and that this method has good robustness and high universality. It is more stable than methods based on wireless signals. Compared with one existing HAR-based method and air pressure, the method in this paper allows pedestrians to undertake long-term static or round-trip activities during the process of going up and down the stairs. In addition, the proposed method has good fault tolerance for the misjudgment of pedestrian actions.

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Accurate and Robust Floor Positioning in Complex Indoor Environments

Sensors ◽

10.3390/s20092698 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2698

Author(s):

Jingyu Huang ◽

Haiyong Luo ◽

Wenhua Shao ◽

Fang Zhao ◽

Shuo Yan

Keyword(s):

Motion Detection ◽

State Of The Art ◽

Barometric Pressure ◽

Indoor Positioning ◽

Location Based Services ◽

Estimation Methods ◽

Indoor Environments ◽

Positioning Method ◽

Application Requirements

With the widespread development of location-based services, the demand for accurate indoor positioning is getting more and more urgent. Floor positioning, as a prerequisite for indoor positioning in multi-story buildings, is particularly important. Though lots of work has been done on floor positioning, the existing studies on floor positioning in complex multi-story buildings with large hollow areas through multiple floors still cannot meet the application requirements because of low accuracy and robustness. To obtain accurate and robust floor estimation in complex multi-story buildings, we propose a novel floor positioning method, which combines the Wi-Fi based floor positioning (BWFP), the barometric pressure-based floor positioning (BPFP) with HMM and the XGBoost based user motion detection. Extensive experiments show that using our proposed method can achieve 99.2% accuracy, which outperforms other state-of-the-art floor estimation methods.

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Anodic Bond Quality and impact on Pressure Sensor Long-Term Stability

MRS Proceedings ◽

10.1557/proc-681-i5.15 ◽

2001 ◽

Vol 681 ◽

Cited By ~ 2

Author(s):

Henry Allen ◽

Kamrul Ramzan ◽

Jim Knutti ◽

Carl Ross ◽

Tim Milliman ◽

...

Keyword(s):

Pressure Sensor ◽

Pressure Sensors ◽

Barometric Pressure ◽

Absolute Pressure ◽

Glass Wafer ◽

Bond Quality ◽

Long Term Stability ◽

Pressure Changes ◽

The Impact

ABSTRACTSilicon pressure sensors have historically been fabricating by bonding a glass wafer to a micro-machined silicon wafer. The sensor may be sealed as an absolute pressure sensor by using planar glass and can then be used for detection of barometric pressure changes.It has generally been assumed that as long as the glass and silicon are reasonable clean, then the silicon-glass seal is good and the part becomes a reliable, stable sensor. This paper addresses a low-level drift that was identified in such an absolute pressure sensor. A Zero drift in the range of 0.1% FS was detectable under humidity stresses. The stress always caused drift in the same direction, indicating an effective increased pressure in the sealed cavity.The impact of various cleaning processes in reducing drift are reported. The improved process assure reliable product for applications such as automotive and altimeter applications.

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ANALISA SENSOR SUHU DAN TEKANAN UDARA TERHADAP KETINGGIAN AIR LAUT BERBASIS MIKROKONTROLER

ANTIVIRUS Jurnal Ilmiah Teknik Informatika ◽

10.35457/antivirus.v13i2.843 ◽

2019 ◽

Vol 13 (2) ◽

pp. 98-104

Author(s):

Fitroh Amaluddin ◽

Andy Haryoko

Keyword(s):

Sea Level ◽

Wave Height ◽

Air Temperature ◽

Barometric Pressure ◽

Water Levels ◽

Air Pressure ◽

Infrared Sensors ◽

Height Measurement ◽

Pressure Changes ◽

Sea Wave

Tsunamis are natural events that can occur any time without prior warning. Some mitigation efforts both through physical construction consist of sea wave height detection sensors such as DT-Sense Barometric Pressure & Temperature sensors, Infrared sensors, and ultrasonic sensors. However, the sensors have a low accuration and difficult installation. Therefore a device designed to provide temperature and air pressure data based on a microcontroller with higher accuracy, and easier installation. The device are made using a DS18B20 temperature sensor, then air pressure using BMP180 sensor. Sea wave height measurement system based on the working principle of air pressure at sea level. This tool is able to work well at altitudes with a minimum temperature of 25 degrees Celsius. Based on the results of air trials on water levels obtained every 0.1 meter increase in sea air, air pressure increases by 0.02 mb (millibar) or 0.12 mb / meter. While testing the air pressure against the temperature obtained is higher, the air temperature at sea level will increase. Each time the air pressure changes by 1.00 mb, the air temperature at sea level will change an average of around 0.46 degrees Celsius. In other words if the temperature decreases around 1 degree Celsius, then the air pressure also drops by 2.00 mb or around 16.67 meters.

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Measuring patient engagement among cancer patients in eHealth and mHealth interventions on symptom management: an integrated review (Preprint)

10.2196/preprints.17966 ◽

2020 ◽

Author(s):

Juqing Zhao ◽

Pei Chen ◽

Guangming Wan

Keyword(s):

Cancer Patients ◽

Patient Engagement ◽

Symptom Management ◽

Subjective Experience ◽

Participation Rate ◽

Sensor Data ◽

Quality Of Data ◽

Mhealth Intervention

BACKGROUND There has been an increase number of eHealth and mHealth interventions aimed to support symptoms among cancer survivors. However, patient engagement has not been guaranteed and standardized in these interventions. OBJECTIVE The objective of this review was to address how patient engagement has been defined and measured in eHealth and mHealth interventions designed to improve symptoms and quality of life for cancer patients. METHODS Searches were performed in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify eHealth and mHealth interventions designed specifically to improve symptom management for cancer patients. Definition and measurement of engagement and engagement related outcomes of each intervention were synthesized. This integrated review was conducted using Critical Interpretive Synthesis to ensure the quality of data synthesis. RESULTS A total of 792 intervention studies were identified through the searches; 10 research papers met the inclusion criteria. Most of them (6/10) were randomized trial, 2 were one group trail, 1 was qualitative design, and 1 paper used mixed method. Majority of identified papers defined patient engagement as the usage of an eHealth and mHealth intervention by using different variables (e.g., usage time, log in times, participation rate). Engagement has also been described as subjective experience about the interaction with the intervention. The measurement of engagement is in accordance with the definition of engagement and can be categorized as objective and subjective measures. Among identified papers, 5 used system usage data, 2 used self-reported questionnaire, 1 used sensor data and 3 used qualitative method. Almost all studies reported engagement at a moment to moment level, but there is a lack of measurement of engagement for the long term. CONCLUSIONS There have been calls to develop standard definition and measurement of patient engagement in eHealth and mHealth interventions. Besides, it is important to provide cancer patients with more tailored and engaging eHealth and mHealth interventions for long term engagement.

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A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows

Applied Sciences ◽

10.3390/app9224813 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4813 ◽

Cited By ~ 2

Author(s):

Hanbo Yang ◽

Fei Zhao ◽

Gedong Jiang ◽

Zheng Sun ◽

Xuesong Mei

Keyword(s):

Time Windows ◽

Remaining Useful Life ◽

Sensor Data ◽

Time Interval ◽

Kernel Module ◽

Initial Stage ◽

Research Task ◽

Consecutive Time ◽

Useful Life

Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.

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Air Movement in Snow Due to Windpumping

Journal of Glaciology ◽

10.3189/s0022143000004524 ◽

1989 ◽

Vol 35 (120) ◽

pp. 209-213 ◽

Cited By ~ 153

Author(s):

S.C. Colbeck

Keyword(s):

Pore Space ◽

Barometric Pressure ◽

Surface Flows ◽

Wind Speeds ◽

Compression And Expansion ◽

Strong Winds ◽

Pressure Changes ◽

Low Amplitude ◽

Pressure Perturbations ◽

Rapid Pressure

Abstract Strong winds can disrupt the thermal regime in seasonal snow because of the variation in surface pressure associated with surface features like dunes and ripples. Topographical features of shorter wavelengths produce stronger surface flows, but the flow decays rapidly with depth. Longer-wavelength features produce weaker surface flows but the flow decays more slowly with depth. The flow may only be strong enough to disrupt the temperature field for features of wavelengths on the scale of meters or tens of meters at wind speeds of 10 m/s or more. Other possible causes of windpumping have been examined but they do not appear to be as significant. Rapid pressure perturbations due to turbulence produce very little displacement of the air because of the high frequency and low amplitude. Barometric pressure changes cause compression and expansion of the air in the pore space, but the rate is too low to have much effect.

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