Journal of Ambient Intelligence and Smart Environments

Ensuring mobility of the elderly is an important task in our aging society. To this end, this paper presents SmartWalker, a high-tech extension of a regular walker that aims to navigate around its environment autonomously and assist its user intelligently. The walker is equipped with sensors and actuators and operates in two modes, autonomous and assistive. In the autonomous mode, the walker accepts gesture commands via its gesture-based interface and navigates around accordingly. The interface uses a k-nearest neighbors classifier with dynamic time warping to recognize gestures and the Viola and Jones face detector to locate the user. In the assistive mode, its automatic speed controller determines the optimal speed for the walker. The walker locates its user by detecting the user’s legs using a laser range scanner and combines the information with other sensory data for the speed control. The walker was evaluated with residents of retirement homes in Zürich. The study aimed to understand the usefulness of robotic walker to the elderly, evaluate its potential to replace traditional walkers, and determine the appropriateness of the added functionality. The elderly found the SmartWalker’s autonomy useful and exciting and the walker with the controller slightly more comfortable and easier to maneuver. They stated that the walker is too big and too heavy but liked it more than traditional walkers in the assistive mode.

Automatic human Activity Recognition (AR) is an important process for the provision of context-aware services in smart spaces such as voice-controlled smart homes. This paper presents an on-line Activities of Daily Living (ADL) recognition method for automatic identification within homes in which multiple sensors, actuators and automation equipment coexist, including audio sensors. Three sequence-based models are presented and compared: a Hidden Markov Model (HMM), Conditional Random Fields (CRF) and a sequential Markov Logic Network (MLN). These methods have been tested in two real Smart Homes thanks to experiments involving more than 30 participants. Their results were compared to those of three non-sequential models: a Support Vector Machine (SVM), a Random Forest (RF) and a non-sequential MLN. This comparative study shows that CRF gave the best results for on-line activity recognition from non-visual, audio and home automation sensors.

In the last decade, the study of human behaviour activities within the field of Ambient Assisted Living (AAL) has led to the emergence of a variety of techniques to learn, detect, and recognize human activities in monitored environments. Among them, one of the most accepted ones is Hidden Markov models (HMM). Activity learning is usually carried out offline, and the current design methodology leads to obtaining a model for each activity of interest. On the other hand, activity recognition should be performed online. Then, if the number of learnt behaviours is high, the amount of computation increases exponentially due to the increase of models to be tested in each time instant, and it might overload the system or provide activity detection speeds far from a real time execution. This problem is increased when, instead of using passive sensors, other devices such as video cameras are used, where the received amount of data per second is much higher. In this paper, it is proposed a new technique to achieve rapid learning and effective activities recognition in real time. Being compared with the baseline technique used with HMM, our proposal is able to improve both the rate of correct activity recognition and the training and detection time complexity, achieving real-time action recognition. The proposed approach is evaluated on two action recognition datasets, one created by our team and another more challenging external dataset including activities of daily life.

The public health implications of growing numbers of older adults at risk for dementia places pressure on identifying dementia at its earliest stages so as to develop proactive management plans. The prodromal dementia phase commonly identified as mild cognitive impairment is an important target for this early detection of impending dementia amenable to treatment. In this paper, we propose a method for home-based automatic detection of mild cognitive impairment in older adults through continuous monitoring via unobtrusive sensing technologies. Our method is composed of two main stages: a training stage and a test stage. For training, room activity distributions are estimated for each subject using a time frame of ω weeks, and then affinity propagation is employed to cluster the activity distributions and to extract exemplars to represent the different emerging clusters. For testing, room activity distributions belonging to a test subject with unknown cognitive status are compared to the extracted exemplars and get assigned the labels of the exemplars that result in the smallest normalized Kullbak–Leibler divergence. The labels of the activity distributions are then used to determine the cognitive status of the test subject. Using the sensor and clinical data pertaining to 85 homes with single occupants, we were able to automatically detect mild cognitive impairment in older adults with an F 0.5 score of 0.856. Also, we were able to detect the non-amnestic sub-type of mild cognitive impairment in older adults with an F 0.5 score of 0.958.

One in three elders over the age of 65 falls each year in the United States. This paper describes a non-invasive fall detection system based on a Doppler radar sensor. The developed system has been tested in two environments: laboratory and real senior living apartments. While some laboratory results appeared in our previous papers, the main novelty of this paper consists in the deployment of our fall detection system in six apartments from TigerPlace (a senior living facility in Columbia, Missouri). The fall detection results obtained in our laboratory were excellent, with the radar placed on the ceiling performing better than on the floor. The fall detection system was then evaluated using radar data collected over two weeks in six TigerPlace apartments. The fall detection system successfully detected all six natural senior falls in an apartment for the examined one week.

Intelligent environments collect and process personal information to assist individuals with their daily activities, enhance their experiences and adapt to their needs and intentions. The prosperity of paradigms like the Internet of Things (IoT) will boost the development of intelligent environments, but the envisioned exponential data growth will give rise to serious security and privacy concerns. Already in the domains of smart homes and healthcare one can observe a growing trend of intelligent environments being extended with third party smart service and technology providers – such as cloud and Big Data analytics services – that analyze and visualize sensitive information as a means to offer new insights to their customers, but that typically cross the personal space or privacy boundaries of the intelligent environment. The challenge addressed in this work is how to offer Big Data processing capabilities as a service with appropriate data protection safeguards in order to protect the individual’s privacy in the extended intelligent environment.

In this paper, we present SparkXS, a framework which offers granular and scalable access and data protection control on streaming data that can deal with the growing velocity, volume and variety of volatile data of IoT, integrated on top of our SAMURAI lambda architecture for Big Data processing. Driven by upcoming legislation and obligations, such as the EU General Data Protection Regulation (GDPR), our framework applies Privacy by Design (PbD) strategies and offers security controls that empower users to better control their personal data. Experimental results with motivating use cases and large data sets demonstrate the feasibility and scalability of our SparkXS framework while operating with acceptable performance overheads.