scholarly journals Optimizing Sensor Deployment for Multi-Sensor-Based HAR System with Improved Glowworm Swarm Optimization Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7161
Author(s):  
Yiming Tian ◽  
Jie Zhang
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Good Choice ◽  
Learning System ◽  
Sensor Deployment ◽  
Swarm Optimization ◽  
Large Power ◽  
Concurrent Use ◽  
Glowworm Swarm Optimization ◽  
Selective Ensemble

Human activity recognition (HAR) technology that analyzes and fuses the data acquired from various homogeneous or heterogeneous sensor sources has motivated the development of enormous human-centered applications such as healthcare, fitness, ambient assisted living and rehabilitation. The concurrent use of multiple sensor sources for HAR is a good choice because the plethora of user information provided by the various sensor sources may be useful. However, a multi-sensor system with too many sensors will bring large power consumption and some sensor sources may bring little improvements to the performance. Therefore, the multi-sensor deployment research that can gain a tradeoff among computational complexity and performance is imperative. In this paper, we propose a multi-sensor-based HAR system whose sensor deployment can be optimized by selective ensemble approaches. With respect to optimization of the sensor deployment, an improved binary glowworm swarm optimization (IBGSO) algorithm is proposed and the sensor sources that have a significant effect on the performance of HAR are selected. Furthermore, the ensemble learning system based on optimized sensor deployment is constructed for HAR. Experimental results on two datasets show that the proposed IBGSO-based multi-sensor deployment approach can select a smaller number of sensor sources while achieving better performance than the ensemble of all sensors and other optimization-based selective ensemble approaches.

scholarly journals Towards a Simulation Framework for Smart Indoor Spaces

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7137
Author(s):  
Shadan Golestan ◽  
Ioanis Nikolaidis ◽  
Eleni Stroulia
Keyword(s):  
Assisted Living ◽  
Daily Living ◽  
Time Window ◽  
Ambient Assisted Living ◽  
Sensor Deployment ◽  
Simulation Framework ◽  
Simulation Based ◽  
Different Types ◽  
Indoor Spaces ◽  
Over Time

The effectiveness of sensor-based applications for smart homes and smart buildings is conditioned upon the deployment configuration of their underlying sensors. Real-world evaluation of alternative possible sensor-deployment configurations is labor-intensive, costly, and time-consuming, which implies the need for a simulation-based methodology. In this work, we report on such a methodology that supports the modeling of indoor spaces, the activities of their occupants, and the behaviors of different types of sensors. We argue that, in order for a simulation to be useful for the purpose of evaluating a sensor deployment configuration, it has to generate realistic event streams of individual sensors over time, as well as realistic compositions of sensor events within a time window. We have evaluated our simulator for smart indoor spaces, SIMsis toolkit, in the context of our Smart-Condo ambient-assisted living platform, supporting the observation and analysis of activities of daily living (ADLs). Our findings indicate that SIMsis produces realistic agent traces and sensor readings, and has the potential to support the process of developing and deploying sensor-based applications.

scholarly journals Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3468 ◽  
Author(s):  
Tian ◽  
Zhang ◽  
Chen ◽  
Geng ◽  
Wang
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
The Body ◽  
Swarm Optimization ◽  
Diversity Measure ◽  
Base Classifier ◽  
Glowworm Swarm Optimization ◽  
Selective Ensemble

Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap sampling method is utilized to independently train multiple base ELMs which make up the initial base classifier pool. Secondly, the initial pool is pre-pruned by calculating the pairwise diversity measure of each base ELM, which can eliminate similar base ELMs and enhance the performance of HAR system by balancing diversity and accuracy. Then, glowworm swarm optimization (GSO) is utilized to search for the optimal sub-ensemble from the base ELMs after pre-pruning. Finally, majority voting is utilized to combine the results of the selected base ELMs. For the evaluation of our proposed method, we collected a dataset from different locations on the body, including chest, waist, left wrist, left ankle and right arm. The experimental results show that, compared with traditional ensemble algorithms such as Bagging, Adaboost, and other state-of-the-art pruning algorithms, the proposed approach is able to achieve better performance (96.7% accuracy and F1 from wrist) with fewer base classifiers.

Ambient Assisted Living Systems: The Scope of Research and Development

2017 ◽  
Vol 3 (2) ◽  
pp. 21
Author(s):  
Ashish D Patel ◽  
Jigarkumar H. Shah
Keyword(s):  
Assisted Living ◽  
Independent Living ◽  
Intelligent Systems ◽  
New Technologies ◽  
Ambient Assisted Living ◽  
Smart Devices ◽  
Economic Changes ◽  
Aged Population ◽  
The People ◽  
Living Person

The aged population of the world is increasing by a large factor due to the availability of medical and other facilities. As the number grows rapidly, requirements of this segment of age (65+) are increasing rapidly as well as the percentage of aged persons living alone is also increasing with the same rate due to the inevitable socio-economic changes. This situation demands the solution of many problems like loneliness, chronic conditions, social interaction, transportation, day-to-day life and many more for independent living person. A large part of aged population may not be able to interact directly with new technologies. This sought some serious development towards the use of intelligent systems i.e. smart devices which helps the people with their inability to use the available as well future solutions. Ambient Assisted Living (AAL) is the answer to these problems. In this paper, issues related to AAL systems are studied. Study of challenges and limitations of this comparatively new field will help the designers to remove the barriers of AAL systems.

scholarly journals Contactless Interactive Fall Detection and Sleep Quality Estimation for Supporting Elderly with Incipient Dementia

2020 ◽  
Vol 6 (3) ◽  
pp. 388-391
Author(s):  
Roman Siedel ◽  
Tobias Scheck ◽  
Ana C. Perez Grassi ◽  
Julian B. Seuffert ◽  
André Apitzsch ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Quality Estimation ◽  
Emergency Situations ◽  
People With Dementia ◽  
Affected Person ◽  
Development State ◽  
Current Behaviour

AbstractIn recent years, the demographic change in conjunction with a lack of professional caregivers led to retirement homes reaching capacity. The Alzheimer Disease International stated that over 50 million people suffered from dementia in 2019 worldwide and twice the amount will presumably be effected in 2030. The field of Ambient Assisted Living (AAL) tackles this problem by facilitating technical system-aided everyday life. AUXILIA is such an AAL system and does not only support elderly people with dementia in an early phase, but also monitors their activities to provide behaviour analysis results for care attendants, relatives and physicians. Moreover, the system is capable of recognizing emergency situations like human falls. Furthermore, sleep quality estimation is employed to be able to draw conclusions about the current behaviour of an affected person. This article presents the current development state of AUXILIA.

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