scholarly journals SEAbIRD: Adaptable Daily Living Activity Identification from Sensor Data Streams

2018 ◽  
Vol 130 ◽  
pp. 939-946
Author(s):  
José Molano-Pulido ◽  
Claudia Jiménez-Guarín
Keyword(s):  
Data Streams ◽  
Daily Living ◽  
Sensor Data ◽  
Daily Living Activity

scholarly journals Sensor Fusion for Recognition of Activities of Daily Living

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4029 ◽  
Author(s):  
Jiaxuan Wu ◽  
Yunfei Feng ◽  
Peng Sun
Keyword(s):  
Time Series ◽  
Sensor Fusion ◽  
Daily Living ◽  
Single Point ◽  
Recognition System ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Life Patterns ◽  
Functional Capabilities ◽  
Correction Technique

Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.

scholarly journals Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881130 ◽  
Author(s):  
Jaanus Kaugerand ◽  
Johannes Ehala ◽  
Leo Mõtus ◽  
Jürgo-Sören Preden
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Streams ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Temporal Consistency ◽  
Variable Delays

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

Sensor Data Streams

2014 ◽  
pp. 291-321 ◽  
Author(s):  
Stephen Voida ◽  
Donald J. Patterson ◽  
Shwetak N. Patel
Keyword(s):  
Data Streams ◽  
Sensor Data

Type A Behavior and Daily Living Activity among Older Minority Women

2002 ◽  
Vol 11 (2) ◽  
pp. 137-146 ◽  
Author(s):  
Karen E. Eason ◽  
Louise C. Mâsse ◽  
Susan R. Tortolero ◽  
Steven H. Kelder
Keyword(s):  
Daily Living ◽  
Type A ◽  
Type A Behavior ◽  
Minority Women ◽  
Daily Living Activity

Providing Persistence for Sensor Data Streams by Remote WAL

2006 ◽  
pp. 524-533
Author(s):  
Hideyuki Kawashima ◽  
Michita Imai ◽  
Yuichiro Anzai
Keyword(s):  
Data Streams ◽  
Sensor Data

A solution for annotating sensor data streams - An industrial use case in building management system

2020 ◽  
Author(s):  
Dumindu Madithiyagasthenna ◽  
Prem Prakash Jayaraman ◽  
Ahsan Morshed ◽  
Abdur Rahim Mohammad Forkan ◽  
Dimitrios Georgakopoulos ◽  
...  
Keyword(s):  
Data Streams ◽  
Management System ◽  
Sensor Data ◽  
Use Case ◽  
Building Management ◽  
Building Management System ◽  
Industrial Use

Knowledge Discovery for Sensor Network Comprehension

2010 ◽  
pp. 118-135 ◽  
Author(s):  
Pedro Pereira Rodrigues ◽  
João Gama ◽  
Luís Lopes
Keyword(s):  
Sensor Networks ◽  
Knowledge Discovery ◽  
Sensor Network ◽  
Data Streams ◽  
High Rate ◽  
Sensor Data ◽  
Processing Abilities ◽  
Comprehension Ability ◽  
Visualization Of Data ◽  
Different Characteristics

In this chapter we explore different characteristics of sensor networks which define new requirements for knowledge discovery, with the common goal of extracting some kind of comprehension about sensor data and sensor networks, focusing on clustering techniques which provide useful information about sensor networks as it represents the interactions between sensors. This network comprehension ability is related with sensor data clustering and clustering of the data streams produced by the sensors. A wide range of techniques already exists to assess these interactions in centralized scenarios, but the seizable processing abilities of sensors in distributed algorithms present several benefits that shall be considered in future designs. Also, sensors produce data at high rate. Often, human experts need to inspect these data streams visually in order to decide on some corrective or proactive operations (Rodrigues & Gama, 2008). Visualization of data streams, and of data mining results, is therefore extremely relevant to sensor data management, and can enhance sensor network comprehension, and should be addressed in future works.

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