Abstract--Fall detection of the elderly is a major public health problem. Thus it has generated a wide range of applied research and prompted the development of telemonitoring systems to enable the early diagnosis of fall conditions. This article is a survey of systems, algorithms and sensors, for the automatic early detection of the fall of elderly persons. It points out the difficulty to compare the performances of the different systems due to the lack of a common framework. It then proposes a procedure for this evaluation.

Abstract-By 2050, about a third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international ADL or the French AGGIR scales, by automatically classifying the different Activities of Daily Living performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, Infra-Red Presence Sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs on postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors, is then used to classify each temporal frame into one of the activities of daily living that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using Support Vector Machines. We performed a one-hour experimentation with 13 young and healthy subjects to determine the models of the different activities and then we tested the classification algorithm (cross-validation) with real data.

Over the years, smartphones have become tools for scientific and clinical research. They can, for instance, be used to assess range of motion and joint angle measurement. In this paper, our aim was to determine if smartphones are reliable and accurate enough for clinical motion research. This work proposes an evaluation of different smartphone sensors performance and different manufacturer algorithm performances with the comparison to the gold standard, an industrial robotic arm with an actual standard use inertial motion unit in clinical measurement, an Xsens product. Both dynamic and static protocols were used to perform these comparisons. Root Mean Square (RMS) mean values results for static protocol are under 0.3° for the different smartphones. RMS mean values results for dynamic protocol are more prone to bias induced by Euler angle representation. Statistical results prove that there are no filter effect on results for both protocols and no hardware effect. Smartphones performance can be compared to the Xsens gold standard for clinical research.

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This article proposes an implementation of a Kalman Filter, using inertial sensors of a Smartphone, to estimate 3D angulation of the trunk. The developped system monitors the trunk angular evolution during bipedal stance and helps the user to improve balance through a configurable and integrated auditory-biofeedback loop. A proof-of-concept study was performed to assess the effectiveness of this so-called iBalance-ABF -smartphone-based audio-biofeedback system -in improving balance during bipedal standing. Results showed that young healthy individuals were able to efficiently use ABF on sagittal trunk tilt to improve their balance in the ML direction. These findings suggest that iBalance-ABF system as a Telerehabilitation system which could represent a suitable solution for Ambient Assisted Living technologies.

The Internet of Things (IoT) is a computing paradigm whereby everyday life objects are augmented with computational and wireless communication capabilities, typically through the incorporation of resource-constrained devices including sensors and actuators, which enable their connection to the Internet. The IoT is seen as the key ingredient for the development of smart environments. Nevertheless, the current IoT ecosystem offers many alternative communication solutions with diverse performance characteristics. This situation presents a major challenge to identifying the most suitable IoT communication solution(s) for a particular smart environment. In this paper we consider the distinct requirements of key smart environments, namely the smart home, smart health, smart cities and smart factories, and relate them to current IoT communication solutions. Specifically, we describe the core characteristics of these smart environments and then proceed to provide a comprehensive survey of relevant IoT communication technologies and architectures. We conclude with our reflections on the crucial features of IoT solutions in this setting and a discussion of challenges that remain open for research.

An online recognition system must analyze the changes in the sensing data and at any significant detection; it has to decide if there is a change in the activity performed by the person. Such a system can use the previous sensor readings for decision-making (decide which activity is performed), without the need to wait for future ones. This paper proposes an approach of human activity recognition on online sensor data. We present four methods used to extract features from the sequence of sensor events. Our experimental results on public smart home data show an improvement of effectiveness in classification accuracy.

Considering the important role of the cervical joint position sense on control of human posture and locomotion, accurate and reliable evaluation of neck proprioceptive abilities appears of great importance. Although the cervicocephalic relocation test (CRT) to the neutral head position (NHP) usually is used for both research and clinical purposes, its test-retest reliability has not been clearly established yet. The purpose of the present experiment was to 1) evaluate the test-retest reliability of the CRT to NHP and 2) to determine the number of trial recordings required to ensure reliable measurements. To this aim, 40 young healthy adults performed the CRT to NHP on two separate occasions. Ten trials were performed for each rotation side. Absolute and variable errors, processed along their horizontal, vertical, and global components, were used to assess the cervical joint repositioning accuracy and consistency, respectively. Mean difference between test and retest with 95% confidence interval, intraclass correlation coefficient, and Bland and Altman graphs with limits of agreement were used as statistical methods for assessing test-retest reliability. Results show that the CRT to NHP when executed in its original form (i.e., 10 trials) has a fair to excellent reliability (ICC ranged from 0.52 to 0.81 and from 0.49 to 0.77, for absolute and variable errors, respectively); the test-retest reliability of this test increases as the number of trials used to establish subject's repositioning errors increases; and using the mean of eight trials is sufficient to ensure fair to excellent reliability of the measurements (ICC ranged from 0.39 to 0.78 and from 0.44 to 0.78, for absolute and variable errors, respectively).