Overcoming barriers and increasing independence: service robots for elderly and disabled people

This paper discusses the potential for service robots to overcome barriers and increase independence of elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly people and advances in technology which will make new uses possible and provides suggestions for some of these new applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses the complementarity of assistive service robots and personal assistance and considers the types of applications and users for which service robots are and are not suitable

A new system to detect coronavirus social distance violation

In this paper, a novel solution to avoid new infections is presented. Instead of tracing users’ locations, the presence of individuals is detected by analysing the voices, and people’s faces are detected by the camera. To do this, two different Android applications were implemented. The first one uses the camera to detect people’s faces whenever the user answers or performs a phone call. Firebase Platform will be used to detect faces captured by the camera and determine its size and estimate their distance to the phone terminal. The second application uses voice biometrics to differentiate the users’ voice from unknown speakers and creates a neural network model based on 5 samples of the user’s voice. This feature will only be activated whenever the user is surfing the Internet or using other applications to prevent undesired contacts. Currently, the patient’s tracking is performed by geolocation or by using Bluetooth connection. Although face detection and voice recognition are existing methods, this paper aims to use them and integrate both in a single device. Our application cannot violate privacy since it does not save the data used to carry out the detection and does not associate this data to people

Exploring the Use of Wearables to Enable Indoor Navigation for Blind Users

One of the challenges that people with visual impairments (VI) have to have to confront daily, is navigating independently through foreign or unfamiliar spaces.Navigating through unfamiliar spaces without assistance is very time consuming and leads to lower mobility. Especially in the case of indoor environments where the use of GPS is impossible, this task becomes even harder.However, advancements in mobile and wearable computing pave the path to new cheap assistive technologies that can make the lives of people with VI easier.Wearable devices have great potential for assistive applications for users who are blind as they typically feature a camera and support hands and eye free interaction. Smart watches and heads up displays (HUDs), in combination with smartphones, can provide a basis for development of advanced algorithms, capable of providing inexpensive solutions for navigation in indoor spaces. New interfaces are also introduced making the interaction between users who are blind and mo-bile devices more intuitive.This work presents a set of new systems and technologies created to help users with VI navigate indoor environments. The first system presented is an indoor navigation system for people with VI that operates by using sensors found in mo-bile devices and virtual maps of the environment. The second system presented helps users navigate large open spaces with minimum veering. Next a study is conducted to determine the accuracy of pedometry based on different body placements of the accelerometer sensors. Finally, a gesture detection system is introduced that helps communication between the user and mobile devices by using sensors in wearable devices

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

A Novel Approach to Complex Human Activity Recognition

Human activity recognition is a technology that offers automatic recognition of what a person is doing with respect to body motion and function. The main goal is to recognize a person\u27s activity using different technologies such as cameras, motion sensors, location sensors, and time. Human activity recognition is important in many areas such as pervasive computing, artificial intelligence, human-computer interaction, health care, health outcomes, rehabilitation engineering, occupational science, and social sciences. There are numerous ubiquitous and pervasive computing systems where users\u27 activities play an important role. The human activity carries a lot of information about the context and helps systems to achieve context-awareness. In the rehabilitation area, it helps with functional diagnosis and assessing health outcomes. Human activity recognition is an important indicator of participation, quality of life and lifestyle. There are two classes of human activities based on body motion and function. The first class, simple human activity, involves human body motion and posture, such as walking, running, and sitting. The second class, complex human activity, includes function along with simple human activity, such as cooking, reading, and watching TV. Human activity recognition is an interdisciplinary research area that has been active for more than a decade. Substantial research has been conducted to recognize human activities, but, there are many major issues still need to be addressed. Addressing these issues would provide a significant improvement in different aspects of the applications of the human activity recognition in different areas. There has been considerable research conducted on simple human activity recognition, whereas, a little research has been carried out on complex human activity recognition. However, there are many key aspects (recognition accuracy, computational cost, energy consumption, mobility) that need to be addressed in both areas to improve their viability. This dissertation aims to address the key aspects in both areas of human activity recognition and eventually focuses on recognition of complex activity. It also addresses indoor and outdoor localization, an important parameter along with time in complex activity recognition. This work studies accelerometer sensor data to recognize simple human activity and time, location and simple activity to recognize complex activity