Learning routines over long-term sensor data using topic models

2013 ◽  
Vol 31 (4) ◽  
pp. 365-377 ◽  
Author(s):  
Federico Castanedo ◽  
Diego López- de-Ipiña ◽  
Hamid K. Aghajan ◽  
Richard Kleihorst
Keyword(s):  
Topic Models ◽  
Sensor Data

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

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.

scholarly journals Research on HAR-Based Floor Positioning

2021 ◽  
Vol 10 (7) ◽  
pp. 437
Author(s):  
Hongxia Qi ◽  
Yunjia Wang ◽  
Jingxue Bi ◽  
Hongji Cao ◽  
Shenglei Xu
Keyword(s):  
Barometric Pressure ◽  
Air Pressure ◽  
Location Based Services ◽  
Sensor Data ◽  
Reference Station ◽  
Position Change ◽  
Radio Signals ◽  
Positioning Method ◽  
Pressure Changes

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.

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

2019 ◽  
Vol 9 (22) ◽  
pp. 4813 ◽  
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.

scholarly journals CARE: Context-awareness for elderly care

2020 ◽  
Author(s):  
Simon Klakegg ◽  
Kennedy Opoku Asare ◽  
Niels van Berkel ◽  
Aku Visuri ◽  
Eija Ferreira ◽  
...  
Keyword(s):  
Nursing Home ◽  
Context Awareness ◽  
Care Service ◽  
Elderly Care ◽  
Sensor Data ◽  
Context Aware ◽  
Integrated Technology ◽  
Potential Impact ◽  
Post Hoc

AbstractWe present CARE, a context-aware tool for nurses in nursing homes. The system utilises a sensors infrastructure to quantify the behaviour and wellbeing (e.g., activity, mood, social and nurse interactions) of elderly residents. The sensor data is offloaded, processed and analysed in the cloud, to generate daily and long-term summaries of residents’ health. These insights are then presented to nurses via an Android tablet application. We aim to create a tool that can assist nurses and increase their awareness to residents’ needs. We deployed CARE in a local nursing home for two months and evaluated the system through a post-hoc exploratory analysis and interviews with the nurses. The results indicate that CARE can reveal essential insights on the wellbeing of elderly residents and improve the care service. In the discussion, we reflect on our understanding and potential impact of future integrated technology in elderly care environments.

Long-term effects of fine particulate PM2.5 estimated from NASA satellite remote sensor data on mortality in an elderly cohort in Hong Kong

2013 ◽  
Vol 2013 (1) ◽  
pp. 4114
Author(s):  
Chit Ming Wong ◽  
Hak Kan Lai ◽  
Hilda Tsang ◽  
Thuan Quoc Thach ◽  
Graham Neil Thomas ◽  
...  
Keyword(s):  
Hong Kong ◽  
Sensor Data ◽  
Long Term Effects ◽  
Fine Particulate ◽  
Remote Sensor ◽  
Elderly Cohort

scholarly journals Potential and Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience

2021 ◽  
Vol 13 (18) ◽  
pp. 3618
Author(s):  
Stefan Dech ◽  
Stefanie Holzwarth ◽  
Sarah Asam ◽  
Thorsten Andresen ◽  
Martin Bachmann ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Observation Time ◽  
Sensor Data ◽  
High Temporal Resolution ◽  
Statistical Parameters ◽  
Medium Resolution ◽  
Long Time ◽  
Earth Observation Satellite

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.

scholarly journals Comparison of Embedded Sensor Data for Long-Term Care Residents Before and After Onset of the COVID-19 Pandemic

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 95-96
Author(s):  
Erin Robinson ◽  
Wenlong Wu ◽  
Geunhye Park ◽  
Gashaye M Tefera ◽  
Kari Lane ◽  
...  
Keyword(s):  
Mental Health ◽  
Older Adults ◽  
Long Term Care ◽  
Sensor Data ◽  
Term Care ◽  
Health Concerns ◽  
Front Door ◽  
Depth Sensors ◽  
Mental Health Concerns

Abstract Older adults have experienced greater isolation and mental health concerns during the COVID-19 pandemic. In long-term care (LTC) settings, residents have been particularly impacted due to strict lockdown policies. Little is known about how these policies have impacted older adults. This study leveraged existing research with embedded sensors installed in LTC settings, and analyzed sensor data of residents (N=30) two months pre/post the onset of the U.S. COVID-19 pandemic (1/13/20 to 3/13/20, 03/14/20 to 5/13/20). Data from three sensors (bed sensors, depth sensors, and motion sensors) were analyzed for each resident using paired t-tests, which generated information on the resident’s pulse, respiration, sleep, gait, and motion in entering/exiting their front door, living rooms, bedrooms, and bathrooms. A 14.4% decrease was observed in front door motion in the two months post-onset of the pandemic, as well as a 2.4% increase in average nighttime respiration, and a 7.6% increase in nighttime bed restlessness. Over half of our sample (68%) had significant differences (p<0.05) in restlessness. These results highlight the potential impact of the COVID-19 pandemic and social distancing policies on older adults living in LTC. While it is not surprising that significant differences were found in the front door motion sensor, the bed sensor data can potentially shed light on how sleep was impacted during this time. As older adults experienced additional mental health concerns during this time, their normal sleep patterns could have been affected. Implications could help inform LTC staff, healthcare providers, and self-management of health approaches among older adults.

Sign in / Sign up

Export Citation Format

Share Document