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

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 redudan bisa berdampak pada konsumsi energi yang tinggi. Penelitian 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 nilai 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. ================================================================================================================================ The development of change detection mechanism has an important role in the Indoor Positioning System (IPS). In IPS technology, a lot of battery power will be used because the WiFi scanning process runs continuously. The WiFi scanning process sends data from the client to the server continuously, sometimes providing the same and repeatable information to the user. Information sent redundantly can have an impact on high energy consumption. In this research, the researchers developed a repair mechanism with change detection to save energy in an adaptive sampling of the strength of the WiFi signal with the accelerometer as a trigger for the adaptive process. Change detection mechanism that is done is measuring the signal strength on the accelerometer by determining the silent zone. Silent Zone is the range of values obtained when the accelerometer is at rest. If it is known that the signal strength value on the Accelerometer exceeds the value of the silent zone, the user is identified in a mobile condition, and the WiFi scanning process will automatically run. Change detection with Bluetooth has the same process using an accelerometer. The algorithm we propose can produce a battery-saving of 4.384% for scanning with change detection using an accelerometer and 2.666% for change detection using Bluetooth

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A survey on wearable sensor modality centred human activity recognition in health care

Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR

A data fusion-based hybrid sensory system for older people’s daily activity recognition.

Population aged 60 and over is growing faster. Ageing-caused changes, such as physical or cognitive decline, could affect people’s quality of life, resulting in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) has become one of the most promising assistive technologies for older people’s daily life. Literature in HAR suggests that each sensor modality has its strengths and limitations and single sensor modalities may not cope with complex situations in practice. This research aims to design and implement a hybrid sensory HAR system to provide more comprehensive, practical and accurate surveillance for older people to assist them living independently. This reseach: 1) designs and develops a hybrid HAR system which provides a spatio- temporal surveillance system for older people by combining the wrist-worn sensors and the room-mounted ambient sensors (passive infrared); the wearable data are used to recognize the defined specific daily activities, and the ambient information is used to infer the occupant’s room-level daily routine; 2): proposes a unique and effective data fusion method to hybridize the two-source sensory data, in which the captured room-level location information from the ambient sensors is also utilized to trigger the sub classification models pretrained by room-assigned wearable data; 3): implements augmented features which are extracted from the attitude angles of the wearable device and explores the contribution of the new features to HAR; 4:) proposes a feature selection (FS) method in the view of kernel canonical correlation analysis (KCCA) to maximize the relevance between the feature candidate and the target class labels and simultaneously minimizes the joint redundancy between the already selected features and the feature candidate, named mRMJR-KCCA; 5:) demonstrates all the proposed methods above with the ground-truth data collected from recruited participants in home settings. The proposed system has three function modes: 1) the pure wearable sensing mode (the whole classification model) which can identify all the defined specific daily activities together and function alone when the ambient sensing fails; 2) the pure ambient sensing mode which can deliver the occupant’s room-level daily routine without wearable sensing; and 3) the data fusion mode (room-based sub classification mode) which provides a more comprehensive and accurate surveillance HAR when both the wearable sensing and ambient sensing function properly. The research also applies the mutual information (MI)-based FS methods for feature selection, Support Vector Machine (SVM) and Random Forest (RF) for classification. The experimental results demonstrate that the proposed hybrid sensory system improves the recognition accuracy to 98.96% after applying data fusion using Random Forest (RF) classification and mRMJR-KCCA feature selection. Furthermore, the improved results are achieved with a much smaller number of features compared with the scenario of recognizing all the defined activities using wearable data alone. The research work conducted in the thesis is unique, which is not directly compared with others since there are few other similar existing works in terms of the proposed data fusion method and the introduced new feature set