AmIE: An Ambient Intelligent Environment for Assisted Living

In the modern world of technology Internet-of-things (IoT) systems strives to provide an extensive interconnected and automated solutions for almost every life aspect. This paper proposes an IoT context-aware system to present an Ambient Intelligence (AmI) environment; such as an apartment, house, or a building; to assist blind, visually-impaired, and elderly people. The proposed system aims at providing an easy-to-utilize voice-controlled system to locate, navigate and assist users indoors. The main purpose of the system is to provide indoor positioning, assisted navigation, outside weather information, room temperature, people availability, phone calls and emergency evacuation when needed. The system enhances the user's awareness of the surrounding environment by feeding them with relevant information through a wearable device to assist them. In addition, the system is voice-controlled in both English and Arabic languages and the information are displayed as audio messages in both languages. The system design, implementation, and evaluation consider the constraints in common types of premises in Kuwait and in challenges, such as the training needed by the users. This paper presents cost-effective implementation options by the adoption of a Raspberry Pi microcomputer, Bluetooth Low Energy devices and an Android smart watch.Comment: 6 pages, 8 figures, 1 tabl

Design of Cloud Robotic Services for Senior Citizens to Improve Independent Living and Personal Health Management

A cloud robotics solution was designed and initially tested with a mobile robotic platform and a smart environment, in order to provide health-care management services to senior citizens and improve their independent living. The solution was evaluated in terms of Quality of Service (QoS) and tested in the realistic scenario of the DomoCasa Living Lab, Peccioli, Italy. In particular, a medication reminding service, a remote home monitoring and a user indoor localization algorithm were outsourced in the cloud and provided to the robots, users and carers. The system acquired data from a smart environment and addressed the robot to the user for service delivery. Experiments showed a service's Reliability of Response at least of the 0.04 % and a Time of Response of the same order of magnitude of the processing time required by the user localization algorithm

Penerapan Kalman Filter Pada Metode Trilaterasi Untuk Peningkatan Akurasi Estimasi Perhitungan Jarak Di Dalam Ruangan

Penelitian tentang posisi maupun jarak suatu obyek di dalam ruangan telah banyak dilakukan. Metode trilaterasi adalah salah satu metode yang dapat dipergunakan untuk menghitung nilai estimasi jarak atau posisi suatu obyek di dalam ruangan, berdasarkan nilai RSSI (Received Signal Strength Indication) yang diterima suatu receiver. Namun, nilai RSSI yang diterima tidak dapat stabil dikarenakan sinyal yang diterima oleh receiver sangat dipengaruhi kondisi lingkungan pada ruangan yang pada umumnya memiliki nilai noise yang cukup tinggi. Sehingga dapat berakibat pada nilai estimasi jarak yang diperoleh menjadi kurang akurat. Sehubungan dengan hal tersebut maka setelah dilakukan perhitungan dengan trilaterasi, dilanjutkan dengan menambahkan metode Kalman Filter untuk meningkatkan nilai akurasi. Penelitian ini menggunakan BLE (Bluetooth Low Energy) sebagai transmitter, sedangkan receiver menggunakan smartphone yang sudah ter-install aplikasi untuk menerima nilai RSSI. Setelah menggunakan Kalman Filter diperoleh peningkatan nilai akurasi sebesar 0, 1 meter dari nilai perhitungan trilateras

An Enhanced Indoor Positioning Method Based on Wi-Fi RSS Fingerprinting

In WiFi-based indoor positioning, the received signal strength (RSS) measurements are commonly used to estimate the mobile user location. However, these measurements significantly fluctuate over time and are susceptible to human movement, multipath and Non-Line-of-Sight (NLOS) propagation, which reduce the location accuracy. In this paper, an enhancement positioning method based on the nearest neighbor algorithm is proposed. The distribution of the RSS samples recorded from several Access Points (APs) are used rather than their average, for reducing the location errors introduced by the RSS variations and the multipath problem. The proposed algorithm, named the Nearest Kth Nearest Neighbor (NK-NN) is experimentally evaluated and compared to other powerful methods. The results show that the proposed method outperforms these methods

Pengembangan Mekanisme Change Detection Untuk Efisiensi Energi Pada Wifi-Based Indoor Positioning System

Pengembangan mekanisme change detection mempunyai peranan penting terhadap Indoor Positioning System (IPS). Namun permasalahan yang masih umum dijumpai adalah konsumsi energi yang tinggi, karena proses WiFi scanning berjalan secara terus menerus. Proses WiFi scanning mengirimkan data dari klien ke server secara terus menerus, terkadang memberikan informasi yang sama dan berulang kepada user. Informasi yang dikirim secara redundansi bisa berdampak pada konsumsi energi yang tinggi. Paper ini mengusulkan mekanisme perbaikan dengan change detection untuk penghematan energi dalam melakukan sampling secara adaptif pada kekuatan sinyal WiFi dengan accelerometer sebagai trigger. Mekanisme change detection yang dilakukan adalah mengukur kekuatan sinyal pada accelerometer dengan menentukan silent zone. Silent Zone merupakan rentang nilai yang didapatkan ketika accelerometer dalam kondisi diam. Apabila diketahui nilai kekuatan sinyal pada accelerometer melebihi nilai silent zone, maka diidentifikasi user dalam kondisi bergerak dan secara otomatis proses WiFi scanning akan berjalan. Change detection dengan Bluetooth mempunyai proses yang sama dengan menggunakan accelerometer. Algoritma yang diusulkan dapat menghasilkan penghematan daya baterai sebesar  4,384% untuk scanning dengan change detection menggunakan accelerometer dan 2,666% untuk change detection menggunakan Bluetooth

An IoT-aware AAL System to Capture Behavioral Changes of Elderly People

The ageing of population is a phenomenon that is affecting the majority of developed countries around the world and will soon affect developing economies too. In recent years, both industry and academia are focused on the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. In this context, the behavioral analysis of elderly people can help to prevent the occurrence of Mild Cognitive Impairment (MCI) and frailty problems. The innovative technologies enabling the Internet of Things (IoT) can be used in order to capture personal data for automatically recognizing changes in elderly people behavior in an unobtrusive, low-cost and low-power modality. This work aims to describe the ongoing activities within the City4Age project, funded by the Horizon 2020 Programme of the European Commission, mainly focused on the use of IoT technologies to develop an innovative AAL system able to capture personal data of elderly people in their home and city environments. The proposed architecture has been validated through a proof-of-concept focused mainly on localization issues, collection of ambient parameters, and user-environment interaction aspects

An indoor positioning system using Bluetooth Low Energy

In this paper, we present a Bluetooth Low Energy (BLE) based indoor positioning system developed for monitoring the daily living pattern of old people (e.g. people living with dementia) or individuals with disabilities. The proposed sensing system is composed of multiple sensors that are installed in different locations in a home environment. The specific location of the user in the building has been pre-recorded into the proposed sensing system that captures the raw Received Signal Strength Indicator (RSSI) from the BLE beacon that is attached on the user. Two methods are proposed to determine the indoor location and the tracking of the users: a trilateration-based method and fingerprinting-based method. Experiments have been carried out in different home environments to verify the proposed system and methods. The results show that our system is able to accurately track the user location in home environments and can track the living patterns of the user which, in turn, may be used to infer the health status of the user. Our results also show that the positions of the BLE beacons on the user and different quality of BLE beacons do not affect the tracking accuracy