http://mpc.ece.utexas.edu/Papers/ComparitiveEvaluationOfRSSI.pdf

Comparative evaluation of Received Signal-Strength Index (RSSI) based indoor localization techniques for construction jobsites

With the advancement of wireless technologies and sensing methodologies, many studies have shown the success of re-using wireless signals (e.g., WiFi) to sense human activities and thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, gesture recognition to vital signs monitoring and user identification involving even finer-grained motion sensing. These applications arguably can brace various domains for smart home and office environments, including safety protection, well-being monitoring/management, smart healthcare and smart-appliance interaction. The movements of the human body impact the wireless signal propagation (e.g., reflection, diffraction and scattering), which provide great opportunities to capture human motions by analyzing the received wireless signals. Researchers take the advantage of the existing wireless links among mobile/smart devices (e.g., laptops, smartphones, smart thermostats, smart refrigerators and virtual assistance systems) by either extracting the ready-to-use signal measurements or adopting frequency modulated signals to detect the frequency shift. Due to the low-cost and non-intrusive sensing nature, wireless-based human activity sensing has drawn considerable attention and become a prominent research field over the past decade. In this paper, we survey the existing wireless sensing systems in terms of their basic principles, techniques and system structures. Particularly, we describe how the wireless signals could be utilized to facilitate an array of applications including intrusion detection, room occupancy monitoring, daily activity recognition, gesture recognition, vital signs monitoring, user identification and indoor localization. The future research directions and limitations of using wireless signals for human activity sensing are also discussed.

Wireless Sensing for Human Activity: A Survey

A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living

Describing user activity plays an essential role in ambient intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques. We focus on context ontologies whose ultimate goal is the tracking of human behavior. After studying upper and domain ontologies, both useful for human activity representation and inference, we establish an evaluation criterion to assess the suitability of the different candidate ontologies for this purpose. As a result, any missing features, which are relevant for modeling daily human behaviors, are identified as future challenges.

https://dl.acm.org/doi/pdf/10.1145/2523819

A survey on ontologies for human behavior recognition

Research into ambient assisted living (AAL) strives to ease the daily lives of people with disabilities or chronic medical conditions. AAL systems typically consist of multitudes of sensors and embedded devices, generating large amounts of medical and ambient data. However, these biomedical sensors lack the processing power to perform key monitoring and data-aggregation tasks, necessitating data transmission and computation at central locations. The focus here is on the development of a scalable and context-aware framework and easing the flow between data collection and data processing. The resource-constrained nature of typical wearable body sensors is factored into our proposed model, with cloud computing features utilized to provide a real-time assisted-living service. With the myriad of distributed AAL systems at play, each with unique requirements and eccentricities, the challenge lies in the need to service these disparate systems with a middleware layer that is both coherent and flexible. There is significant complexity in the management of sensor data and the derivation of contextual information, as well as in the monitoring of user activities and in locating appropriate situational services. The proposed CoCaMAAL model seeks to address such issues and implement a service-oriented architecture (SOA) for unified context generation. This is done by efficiently aggregating raw sensor data and the timely selection of appropriate services using a context management system (CMS). With a unified model that includes patients, devices, and computational servers in a single virtual community, AAL services are enhanced. We have prototyped the proposed model and implemented some case studies to demonstrate its effectiveness.

CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living

We propose a novel localization algorithm of mobile sensors based on wireless sensor networks providing RSSI measurements between the mobile and the fixed sensors (anchors) in the network. The algorithm selects and weights the RSSI measurements according to their strength, and it uses a propagation model to transform RSSI measurements into distances, in order to estimate the position of the mobile. The algorithm also uses a virtual calibration method of the propagation model that does not require human intervention. By an experimental setup we show that the localization algorithm increases the performance with respect to the commonly used least mean square algorithm showing also how to achieve a wished accuracy increasing the anchor density.

A Novel Approach to Indoor RSSI Localization by Automatic Calibration of the Wireless Propagation Model

Localization systems based on Received Signal Strength Indicator (RSSI) exploit fingerprinting (based on extensive signal strength measurements) to calibrate the system parameters. This procedure is very expensive in terms of time as it relies on human operators. In this paper we propose a virtual calibration procedure which only exploits the measurements of the RSSI between pairs of anchors. In particular, we propose two procedures for virtual calibration and we evaluate their performance with respect to an ad-hoc calibration campaign by performing measures in an indoor environment with an IEEE 802.15.4 sensor network.

Virtual Calibration for RSSI-Based Indoor Localization with IEEE 802.15.4

User activity monitoring is a major problem in ambient assisted living, since it requires to infer new knowledge from collected and fused sensor data while dealing with highly dynamic environments, where devices continuously change their availability and (or) physical location. In the context of the European project PERSONA, we have developed an activity monitoring sub-system characterized by high modularity, little invasiveness of the environment and good responsiveness. In this paper we first illustrate the functional architecture of the proposed solution from a general point of view, discussing the motivations of the design. Then we describe in details the software components—sensor abstraction and integration layer, human posture classification, activity monitor—and the resulting activity monitoring application, presenting also a performance evaluation.

Sensor data fusion for activity monitoring in the PERSONA ambient assisted living project

The localization methods based on received signal strength indicator (RSSI) link the RSSI values to the position of the mobile to be located. In the RSSI localization techniques based on propagation models, the accuracy depends on the tuning of the propagation models parameters. In indoor wireless networks, the propagation conditions are hardly predictable due to the dynamic nature of the RSSI, and consequently the parameters of the propagation model may change. In this paper, we present an automatic virtual calibration method of the propagation model that does not require human intervention; therefore, can be periodically performed, following the wireless channel conditions. We also propose a novel RSSI-based localization algorithm that selects the RSSI values according to their strength, and uses a calibrated propagation model to transform these values into distances, in order to estimate the position of the mobile. Copyright © 2011 John Wiley & Sons, Ltd.

(We propose an automatic calibration procedure of the signal propagation model that is only based on the RSSIs measured among the anchors and that can be executed periodically and automatically (i.e., without human intervention). Based on the virtual calibration procedure, we propose a localization algorithm that selects the anchors with higher signal strength, and it exploits the calibrated propagation model to relate RSSI measures with distances. Finally, in order to provide the mobile position, the algorithm uses a trilateration method.)

Automatic virtual calibration of range-based indoor localization systems

Wireless Sensor Networks (WSN) are an important technological support for ambient assisted applications. Up to now most WSN applications are based on ad hoc solutions, and attempts to provide reusable applications are still in their youth. Under this trend of research we propose a layered architecture called SAIL (Sensor Abstraction and Integration Layers) to be used in context aware architectures, and aimed at integrating WSN as context information sources. In the proposed approach, the applications running on WSN can be exposed using either a node centric or a data centric paradigm, and they are interfaced with different access technologies. SAIL is currently encapsulated within the OSGi framework and has been tested on MICAz motes.

Stefano Lenzi

Papers

A Novel Approach to Indoor RSSI Localization by Automatic Calibration of the Wireless Propagation Model

Virtual Calibration for RSSI-Based Indoor Localization with IEEE 802.15.4

Sensor data fusion for activity monitoring in the PERSONA ambient assisted living project

Automatic virtual calibration of range-based indoor localization systems

SAIL: A Sensor Abstraction and Integration Layer for Context Awareness