A Two-Level Approach to Characterizing Human Activities from Wearable Sensor Data

International audienceThe rapid emergence of new technologies in recent decades has opened up a world of opportunities for a better understanding of human mobility and behavior. It is now possible to recognize human movements, physical activity and the environments in which they take place. And this can be done with high precision, thanks to miniature sensors integrated into our everyday devices. In this paper, we explore different methodologies for recognizing and characterizing physical activities performed by people wearing new smart devices. Whether it's smartglasses, smartwatches or smartphones, we show that each of these specialized wearables has a role to play in interpreting and monitoring moments in a user's life. In particular, we propose an approach that splits the concept of physical activity into two sub-categories that we call micro-and macro-activities. Micro-and macro-activities are supposed to have functional relationship with each other and should therefore help to better understand activities on a larger scale. Then, for each of these levels, we show different methods of collecting, interpreting and evaluating data from different sensor sources. Based on a sensing system we have developed using smart devices, we build two data sets before analyzing how to recognize such activities. Finally, we show different interactions and combinations between these scales and demonstrate that they have the potential to lead to new classes of applications, involving authentication or user profiling

Analysis of Android Device-Based Solutions for Fall Detection

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.Ministerio de Economía y Competitividad TEC2013-42711-

From MFN to SFN: Performance Prediction Through Machine Learning

In the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97

An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life

Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson\u2019sDisease

Architectures and synchronization techniques for distributed satellite systems: a survey

Cohesive Distributed Satellite Systems (CDSSs) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSSs. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSSs. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for Distributed Satellite Systems (DSSs). First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSSs in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on Machine Learning (ML). Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.This work was supported by the Luxembourg National Research Fund (FNR), through the CORE Project COHEsive SATellite (COHESAT): Cognitive Cohesive Networks of Distributed Units for Active and Passive Space Applications, under Grant FNR11689919.Award-winningPostprint (published version

Bandwidth and Energy-Efficient Route Discovery for Noisy Mobile Ad-Hoc Networks

Broadcasting is used in on-demand routing protocols to discover routes in Mobile Ad-hoc Networks (MANETs). On-demand routing protocols, such as Ad-hoc On-demand Distance Vector (AODV) commonly employ pure flooding based broadcasting to discover new routes. In pure flooding, a route request (RREQ) packet is broadcast by the source node and each receiving node rebroadcasts it. This continues until the RREQ packet arrives at the destination node. Pure flooding generates excessive redundant routing traffic that may lead to the broadcast storm problem (BSP) and deteriorate the performance of MANETs significantly. A number of probabilistic broadcasting schemes have been proposed in the literature to address BSP. However, these schemes do not consider thermal noise and interference which exist in real life MANETs, and therefore, do not perform well in real life MANETs. Real life MANETs are noisy and the communication is not error free. This research argues that a broadcast scheme that considers the effects of thermal noise, co-channel interference, and node density in the neighbourhood simultaneously can reduce the broadcast storm problem and enhance the MANET performance. To achieve this, three investigations have been carried out: First, the effect of carrier sensing ranges on on-demand routing protocol such as AODV and their impact on interference; second, effects of thermal noise on on-demand routing protocols and third, evaluation of pure flooding and probabilistic broadcasting schemes under noisy and noiseless conditions. The findings of these investigations are exploited to propose a Channel Adaptive Probabilistic Broadcast (CAPB) scheme to disseminate RREQ packets efficiently. The proposed CAPB scheme determines the probability of rebroadcasting RREQ packets on the fly according to the current Signal to Interference plus Noise Ratio (SINR) and node density in the neighbourhood. The proposed scheme and two related state of the art (SoA) schemes from the literature are implemented in the standard AODV to replace the pure flooding based broadcast scheme. Ns-2 simulation results show that the proposed CAPB scheme outperforms the other schemes in terms of routing overhead, average end-to-end delay, throughput and energy consumption

A 'one-size-fits-most' walking recognition method for smartphones, smartwatches, and wearable accelerometers

The ubiquity of personal digital devices offers unprecedented opportunities to study human behavior. Current state-of-the-art methods quantify physical activity using 'activity counts,' a measure which overlooks specific types of physical activities. We proposed a walking recognition method for sub-second tri-axial accelerometer data, in which activity classification is based on the inherent features of walking: intensity, periodicity, and duration. We validated our method against 20 publicly available, annotated datasets on walking activity data collected at various body locations (thigh, waist, chest, arm, wrist). We demonstrated that our method can estimate walking periods with high sensitivity and specificity: average sensitivity ranged between 0.92 and 0.97 across various body locations, and average specificity for common daily activities was typically above 0.95. We also assessed the method's algorithmic fairness to demographic and anthropometric variables and measurement contexts (body location, environment). Finally, we have released our method as open-source software in MATLAB and Python.Comment: 39 pages, 4 figures (incl. 1 supplementary), and 5 tables (incl. 2 supplementary

BLoB: Beating-based Localization for Single-antenna BLE Devices

Low-power wireless communication protocols based on synchronous transmissions have recently gained popularity. In such protocols, packets can be demodulated correctly even though several devices transmit at the same time, which results in high reliability and energy efficiency. A by-product of synchronous transmissions is the beating effect: a sinusoidal pattern of constructive and destructive interference across the received signal. In this paper, we leverage this beating to propose a new localization approach. Specifically, we present BLoB, a system in which multiple anchors transmit packets synchronously using the constant tone extension, an optional bit sequence introduced by BLE 5.1, whose signal is sent with constant amplitude and frequency. We let mobile tags sample the superimposed signal resulting from the synchronous transmissions, and extract peaks in the beating and signal spectrum. These peaks provide key insights about the anchors’ location that complement received signal strength information and allow BLoB to derive a tag’s position with sub-meter accuracy. A key property of BLoB is that both anchors and tags employ a single antenna, in contrast to state-of-the-art localization schemes based on angle of arrival/departure information that require costly and bulky antenna arrays to achieve sub-meter accuracy. We implement BLoB on off-the-shelf BLE devices and evaluate its performance experimentally in both static and mobile settings, and in different environments: office rooms, library, meeting room, and sports hall. Our results show that BLoB can distinguish several anchors in a single synchronous transmission and that it retains a sub-meter localization accuracy even in challenging indoor environments

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa