Assessment of Daily Life Mobility Levels using Wearable Inertial Sensors and Minimun Measured Input Models

Tracking activities during daily life and assessing movement parameters is essential for complementing the information gathered in confined environments such as clinical and physical activity laboratories for the assessment of mobility. Inertial measurement units (IMUs) are used as to monitor the motion of human movement for prolonged periods of time and without space limitations. The focus in this study was to provide a robust, low-cost and an unobtrusive solution for evaluating human motion using a single IMU. First part of the study focused on monitoring and classification of the daily life activities. A simple method that analyses the variations in signal was developed to distinguish two types of activity intervals: active and inactive. Neural classifier was used to classify active intervals; the angle with respect to gravity was used to classify inactive intervals. Second part of the study focused on extraction of gait parameters using a single inertial measurement unit (IMU) attached to the pelvis. Two complementary methods were proposed for gait parameters estimation. First method was a wavelet based method developed for the estimation of gait events. Second method was developed for estimating step and stride length during level walking using the estimations of the previous method. A special integration algorithm was extended to operate on each gait cycle using a specially designed Kalman filter. The developed methods were also applied on various scenarios. Activity monitoring method was used in a PRIN’07 project to assess the mobility levels of individuals living in a urban area. The same method was applied on volleyball players to analyze the fitness levels of them by monitoring their daily life activities. The methods proposed in these studies provided a simple, unobtrusive and low-cost solution for monitoring and assessing activities outside of controlled environments.

Assessment of gait spatio-temporal parameters in neurological disorders using wearable inertial sensors

Movement analysis carried out in laboratory settings is a powerful, but costly solution since it requires dedicated instrumentation, space and personnel. Recently, new technologies such as the magnetic and inertial measurement units (MIMU) are becoming widely accepted as tools for the assessment of human motion in clinical and research settings. They are relatively easy-to-use and potentially suitable for estimating gait kinematic features, including spatio-temporal parameters. The objective of this thesis regards the development and testing in clinical contexts of robust MIMUs based methods for assessing gait spatio-temporal parameters applicable across a number of different pathological gait patterns. First, considering the need of a solution the least obtrusive as possible, the validity of the single unit based approach was explored. A comparative evaluation of the performance of various methods reported in the literature for estimating gait temporal parameters using a single unit attached to the trunk first in normal gait and then in different pathological gait conditions was performed. Then, the second part of the research headed towards the development of new methods for estimating gait spatio-temporal parameters using shank worn MIMUs on different pathological subjects groups. In addition to the conventional gait parameters, new methods for estimating the changes of the direction of progression were explored. Finally, a new hardware solution and relevant methodology for estimating inter-feet distance during walking was proposed. Results of the technical validation of the proposed methods at different walking speeds and along different paths against a gold standard were reported and showed that the use of two MIMUs attached to the lower limbs associated with a robust method guarantee a much higher accuracy in determining gait spatio-temporal parameters. In conclusion, the proposed methods could be reliably applied to various abnormal gaits obtaining in some cases a comparable level of accuracy with respect to normal gait.

”Stress Monitoring via Wearable Electrodermal Sensors”

Stress is a phenomenon that on some level affects everyone’s lives on a daily basis. The autonomic nervous system controls the varying levels of stress at any given time. The responses of the autonomic nervous system adjust the body to cope with changing external and internal conditions. During high-stress situations the body is forced into a state of heightened alertness, which passes when the stressor is removed. The stressor can be any external or internal event that causes the body to respond. Stress is a very versatile phenomenon that can be both a cause and an indicator of other medical conditions, for example cardiovascular disease. Stress detection can therefore be helpful in identifying these conditions and monitoring the overall emotional state of a person. Electrodermal activity (EDA) is one of the most easily implemented ways to monitor the activity of the autonomic nervous system. EDA describes changes occurring in the various electrical properties of the skin, including skin conductivity and resistance. Increased emotional sweating has been proven to be one possible indication of stress. On the surface of the skin, increased sweating translates to increased skin conductivity, which can be observed through EDA measurements. This makes electrodermal activity a very useful tool in a wide range of applications where it is desirable to observe changes in a person’s stress level. EDA can be recorded by using specialized body sensors placed on specific locations on the body. Most commonly used recording sites are the palms of the hands due to the high sweat gland density on those areas. Measurement is done using at least two electrodes attached to the skin, and recording the electrical conductance between them. This thesis implements a prototype of a wireless EDA measurement system. The feasibility of the prototype is also verified with a small group of test subjects. EDA was recorded from the subjects while they were playing a game of Tetris. The goal was to observe variations in the measured EDA that would indicate changes in the subjects’ stress levels during the game. The analysis of the obtained measurement results confirmed the connection between stress and recorded EDA. During the game, random occurrences of lowered skin resistance were clearly observable, which indicates points in the game where the player felt more anxious. A wireless measurement system has the potential of offering more flexible and comfortable long-term measuring of EDA, and could be utilized in a wide range of applications.

Towards the retrieval of accurate OD matrices from Bluetooth data : lessons learned from 2 years of data

The Bluetooth technology is being increasingly used to track vehicles throughout their trips, within urban networks and across freeway stretches. One important opportunity offered by this type of data is the measurement of Origin-Destination patterns, emerging from the aggregation and clustering of individual trips. In order to obtain accurate estimations, however, a number of issues need to be addressed, through data filtering and correction techniques. These issues mainly stem from the use of the Bluetooth technology amongst drivers, and the physical properties of the Bluetooth sensors themselves. First, not all cars are equipped with discoverable Bluetooth devices and the Bluetooth-enabled vehicles may belong to some small socio-economic groups of users. Second, the Bluetooth datasets include data from various transport modes; such as pedestrian, bicycles, cars, taxi driver, buses and trains. Third, the Bluetooth sensors may fail to detect all of the nearby Bluetooth-enabled vehicles. As a consequence, the exact journey for some vehicles may become a latent pattern that will need to be extracted from the data. Finally, sensors that are in close proximity to each other may have overlapping detection areas, thus making the task of retrieving the correct travelled path even more challenging. The aim of this paper is twofold. We first give a comprehensive overview of the aforementioned issues. Further, we propose a methodology that can be followed, in order to cleanse, correct and aggregate Bluetooth data. We postulate that the methods introduced by this paper are the first crucial steps that need to be followed in order to compute accurate Origin-Destination matrices in urban road networks.

Retrieving dynamic origin-destination matrices from Bluetooth data

The Bluetooth technology is being increasingly used, among the Automated Vehicle Identification Systems, to retrieve important information about urban networks. Because the movement of Bluetooth-equipped vehicles can be monitored, throughout the network of Bluetooth sensors, this technology represents an effective means to acquire accurate time dependant Origin Destination information. In order to obtain reliable estimations, however, a number of issues need to be addressed, through data filtering and correction techniques. Some of the main challenges inherent to Bluetooth data are, first, that Bluetooth sensors may fail to detect all of the nearby Bluetooth-enabled vehicles. As a consequence, the exact journey for some vehicles may become a latent pattern that will need to be estimated. Second, sensors that are in close proximity to each other may have overlapping detection areas, thus making the task of retrieving the correct travelled path even more challenging. The aim of this paper is twofold: to give an overview of the issues inherent to the Bluetooth technology, through the analysis of the data available from the Bluetooth sensors in Brisbane; and to propose a method for retrieving the itineraries of the individual Bluetooth vehicles. We argue that estimating these latent itineraries, accurately, is a crucial step toward the retrieval of accurate dynamic Origin Destination Matrices.

Traffic state estimation from partial Bluetooth and volume observations : case study in the Brisbane metropolitan area

The application of the Bluetooth (BT) technology to transportation has been enabling researchers to make accurate travel time observations, in freeway and arterial roads. The Bluetooth traffic data are generally incomplete, for they only relate to those vehicles that are equipped with Bluetooth devices, and that are detected by the Bluetooth sensors of the road network. The fraction of detected vehicles versus the total number of transiting vehicles is often referred to as Bluetooth Penetration Rate (BTPR). The aim of this study is to precisely define the spatio-temporal relationship between the quantities that become available through the partial, noisy BT observations; and the hidden variables that describe the actual dynamics of vehicular traffic. To do so, we propose to incorporate a multi- class traffic model into a Sequential Montecarlo Estimation algorithm. Our framework has been applied for the empirical travel time investigations into the Brisbane Metropolitan region.

Bayesian inference of traffic volumes based on Bluetooth data

The study of the relationship between macroscopic traffic parameters, such as flow, speed and travel time, is essential to the understanding of the behaviour of freeway and arterial roads. However, the temporal dynamics of these parameters are difficult to model, especially for arterial roads, where the process of traffic change is driven by a variety of variables. The introduction of the Bluetooth technology into the transportation area has proven exceptionally useful for monitoring vehicular traffic, as it allows reliable estimation of travel times and traffic demands. In this work, we propose an approach based on Bayesian networks for analyzing and predicting the complex dynamics of flow or volume, based on travel time observations from Bluetooth sensors. The spatio-temporal relationship between volume and travel time is captured through a first-order transition model, and a univariate Gaussian sensor model. The two models are trained and tested on travel time and volume data, from an arterial link, collected over a period of six days. To reduce the computational costs of the inference tasks, volume is converted into a discrete variable. The discretization process is carried out through a Self-Organizing Map. Preliminary results show that a simple Bayesian network can effectively estimate and predict the complex temporal dynamics of arterial volumes from the travel time data. Not only is the model well suited to produce posterior distributions over single past, current and future states; but it also allows computing the estimations of joint distributions, over sequences of states. Furthermore, the Bayesian network can achieve excellent prediction, even when the stream of travel time observation is partially incomplete.

Design of a Wearable Remote Neonatal Health Monitoring Device

In this text we present the design of a wearable health monitoring device capable of remotely monitoring health parameters of neonates for the first few weeks after birth. The device is primarily aimed at continuously tracking the skin temperature to indicate the onset of hypothermia in newborns. A medical grade thermistor is responsible for temperature measurement and is directly interfaced to a microcontroller with an integrated bluetooth low energy radio. An inertial sensor is also present in the device to facilitate breathing rate measurement which has been discussed briefly. Sensed data is transferred securely over bluetooth low energy radio to a nearby gateway, which relays the information to a central database for real time monitoring. Low power optimizations at both the circuit and software levels ensure a prolonged battery life. The device is packaged in a baby friendly, water proof housing and is easily sterilizable and reusable.

Testing the performance and feasibility of Bluetooth communications in pervasive systems

Smart and mobile environments require seamless connections. However, due to the frequent process of ''discovery'' and disconnection of mobile devices while data interchange is happening, wireless connections are often interrupted. To minimize this drawback, a protocol that enables an easy and fast synchronization is crucial. Bearing this in mind, Bluetooth technology appears to be a suitable solution to carry on such connections due to the discovery and pairing capabilities it provides. Nonetheless, the time and energy spent when several devices are being discovered and used at the same time still needs to be managed properly. It is essential that this process of discovery takes as little time and energy as possible. In addition to this, it is believed that the performance of the communications is not constant when the transmission speeds and throughput increase, but this has not been proved formally. Therefore, the purpose of this project is twofold: Firstly, to design and build a framework-system capable of performing controlled Bluetooth device discovery, pairing and communications. Secondly, to analyze and test the scalability and performance of the \emph{classic} Bluetooth standard under different scenarios and with various sensors and devices using the framework developed. To achieve the first goal, a generic Bluetooth platform will be used to control the test conditions and to form a ubiquitous wireless system connected to an Android Smartphone. For the latter goal, various stress-tests will be carried on to measure the consumption rate of battery life as well as the quality of the communications between the devices involved.

Exploring the use of sensors to measure behavioral interactions: an experimental evaluation of using hand trajectories.

Humans appear to be sensitive to relative small changes in their surroundings. These changes are often initially perceived as irrelevant, but they can cause significant changes in behavior. However, how exactly people's behavior changes is often hard to quantify. A reliable and valid tool is needed in order to address such a question, ideally measuring an important point of interaction, such as the hand. Wearable-body-sensor systems can be used to obtain valuable, behavioral information. These systems are particularly useful for assessing functional interactions that occur between the endpoints of the upper limbs and our surroundings. A new method is explored that consists of computing hand position using a wearable sensor system and validating it against a gold standard reference measurement (optical tracking device). Initial outcomes related well to the gold standard measurements (r = 0.81) showing an acceptable average root mean square error of 0.09 meters. Subsequently, the use of this approach was further investigated by measuring differences in motor behavior, in response to a changing environment. Three subjects were asked to perform a water pouring task with three slightly different containers. Wavelet analysis was introduced to assess how motor consistency was affected by these small environmental changes. Results showed that the behavioral motor adjustments to a variable environment could be assessed by applying wavelet coherence techniques. Applying these procedures in everyday life, combined with correct research methodologies, can assist in quantifying how environmental changes can cause alterations in our motor behavior.

Wearable wireless inertial measurement for sports applications

A wearable WIMU (Wireless Inertial Measurement Unit) [1] system for sports applications based on Tyndall's 25mm mote technology [2] has been developed to identify tennis performance determining factors, giving coaches & players improved feedback [3, 4]. Multiple WIMUs transmit player motion data to a PC/laptop via a receiver unit. Internally the WIMUs consist of: an IMU layer with MEMS based sensors; a microcontroller/transceiver layer; and an interconnect layer with supplemental 70g accelerometers and a lithium-ion battery. Packaging consists of a robust ABS plastic case with internal padding, a power switch, battery charging port and status LED with Velcro-elastic straps that are used to attach the device to the player. This offers protection from impact, sweat, and movement of sensors which could cause degradation in device performance. In addition, an important requirement for this device is that it needs to be lightweight and comfortable to wear. Calibration ensures that misalignment of the accelerometer and magnetometer axes are accounted for, allowing more accurate measurements to be made.

Wearable antennas for medical monitoring systems

A wearable silver nano particle inkjet printed antenna suitable for wireless biomedical sensing is presented. The performance is evaluated on a synthetic variable layered phantom test-bed, representative of human tissue for operation in the 868/915 MHz, and 2400 MHz industrial, scientific and medical frequency bands. Antenna radiation efficiency measurements on the phantom were compared with antennas prototyped with copper. Total radiation efficiencies up to ???6.5 dB are reported, with less than 0.5 dB difference in performance between copper and silver nano particle variants, showing promising application for low-cost disposable wireless sensing.

SWORD: an intelligent vibratory wearable device to improve rehabilitation in stroke patients

Anualmente ocorrem cerca de 16 milhões AVCs em todo o mundo. Cerca de metade dos sobreviventes irá apresentar défice motor que necessitará de reabilitação na janela dos 3 aos 6 meses depois do AVC. Nos países desenvolvidos, é estimado que os custos com AVCs representem cerca de 0.27% do Produto Interno Bruto de cada País. Esta situação implica um enorme peso social e financeiro. Paradoxalmente a esta situação, é aceite na comunidade médica a necessidade de serviços de reabilitação motora mais intensivos e centrados no doente. Na revisão do estado da arte, demonstra-se o arquétipo que relaciona metodologias terapêuticas mais intensivas com uma mais proficiente reabilitação motora do doente. Revelam-se também as falhas nas soluções tecnológicas existentes que apresentam uma elevada complexidade e custo associado de aquisição e manutenção. Desta forma, a pergunta que suporta o trabalho de doutoramento seguido inquire a possibilidade de criar um novo dispositivo de simples utilização e de baixo custo, capaz de apoiar uma recuperação motora mais eficiente de um doente após AVC, aliando intensidade com determinação da correcção dos movimentos realizados relativamente aos prescritos. Propondo o uso do estímulo vibratório como uma ferramenta proprioceptiva de intervenção terapêutica a usar no novo dispositivo, demonstra-se a tolerabilidade a este tipo de estímulos através do teste duma primeira versão do sistema apenas com a componente de estimulação num primeiro grupo de 5 doentes. Esta fase validará o subsequente desenvolvimento do sistema SWORD. Projectando o sistema SWORD como uma ferramenta complementar que integra as componentes de avaliação motora e intervenção proprioceptiva por estimulação, é descrito o desenvolvimento da componente de quantificação de movimento que o integra. São apresentadas as diversas soluções estudadas e o algoritmo que representa a implementação final baseada na fusão sensorial das medidas provenientes de três sensores: acelerómetro, giroscópio e magnetómetro. O teste ao sistema SWORD, quando comparado com o método de reabilitação tradicional, mostrou um ganho considerável de intensidade e qualidade na execução motora para 4 dos 5 doentes testados num segundo grupo experimental. É mostrada a versatilidade do sistema SWORD através do desenvolvimento do módulo de Tele-Reabilitação que complementa a componente de quantificação de movimento com uma interface gráfica de feedback e uma ferramenta de análise remota da evolução motora do doente. Finalmente, a partir da componente de quantificação de movimento, foi ainda desenvolvida uma versão para avaliação motora automatizada, implementada a partir da escala WMFT, que visa retirar o factor subjectivo da avaliação humana presente nas escalas de avaliação motora usadas em Neurologia. Esta versão do sistema foi testada num terceiro grupo experimental de cinco doentes.

Automatic measurement of key ski jumping phases and temporal events with a wearable system.

We propose a new method, based on inertial sensors, to automatically measure at high frequency the durations of the main phases of ski jumping (i.e. take-off release, take-off, and early flight). The kinematics of the ski jumping movement were recorded by four inertial sensors, attached to the thigh and shank of junior athletes, for 40 jumps performed during indoor conditions and 36 jumps in field conditions. An algorithm was designed to detect temporal events from the recorded signals and to estimate the duration of each phase. These durations were evaluated against a reference camera-based motion capture system and by trainers conducting video observations. The precision for the take-off release and take-off durations (indoor < 39 ms, outdoor = 27 ms) can be considered technically valid for performance assessment. The errors for early flight duration (indoor = 22 ms, outdoor = 119 ms) were comparable to the trainers' variability and should be interpreted with caution. No significant changes in the error were noted between indoor and outdoor conditions, and individual jumping technique did not influence the error of take-off release and take-off. Therefore, the proposed system can provide valuable information for performance evaluation of ski jumpers during training sessions.