Anita Sant'Anna

A Symbol-Based Approach to Gait Analysis From Acceleration Signals: Identification and Detection of Gait Events and a New Measure of Gait Symmetry

Gait analysis can convey important information about ones physical and cognitive condition. Wearable inertial sensor systems can be used to continuously and unobtrusively assess gait during everyday activities in uncontrolled environments. An important step in the development of such systems is the processing and analysis of the sensor data. This paper presents a symbol-based method used to detect the phases of gait and convey important dynamic information from accelerometer signals. The addition of expert knowledge substitutes the need for supervised learning techniques, rendering the system easy to interpret and easy to improve incrementally. The proposed method is compared to an approach based on peak detection. A new symbol-based symmetry index is created and compared to a traditional temporal symmetry index and a symmetry measure based on cross correlation. The symbol-based symmetry index exemplifies how the proposed method can extract more information from the acceleration signal than previous approaches.

A new measure of movement symmetry in early Parkinson's disease patients using symbolic processing of inertial sensor data

Movement asymmetry is one of the motor symptoms associated with Parkinsons disease (PD). Therefore, being able to detect and measure movement symmetry is important for monitoring the patients condition. The present paper introduces a novel symbol based symmetry index calculated from inertial sensor data. The method is explained, evaluated, and compared to six other symmetry measures. These measures were used to determine the symmetry of both upper and lower limbs during walking of 11 early-to-mid-stage PD patients and 15 control subjects. The patients included in the study showed minimal motor abnormalities according to the unified Parkinsons disease rating scale (UPDRS). The symmetry indices were used to classify subjects into two different groups corresponding to PD or control. The proposed method presented high sensitivity and specificity with an area under the receiver operating characteristic (ROC) curve of 0.872, 9% greater than the second best method. The proposed method also showed an excellent intraclass correlation coefficient (ICC) of 0.949, 55% greater than the second best method. Results suggest that the proposed symmetry index is appropriate for this particular group of patients.

Symbolization of time-series: An evaluation of SAX, Persist, and ACA

Symbolization of time-series has successfully been used to extract temporal patterns from experimental data. Segmentation is an unavoidable step of the symbolization process, and it may be characterized on two domains: the amplitude and the temporal domain. These two groups of methods present advantages and disadvantages each. Can their performance be estimated a priori based on signal characteristics? This paper evaluates the performance of SAX, Persist and ACA on 47 different time-series, based on signal periodicity. Results show that SAX tends to perform best on random signals whereas ACA may outperform the other methods on highly periodic signals. However, results do not support that a most adequate method may be determined a priory.

Assessment of Gait Symmetry and Gait Normality Using Inertial Sensors: In-Lab and In-Situ Evaluation

Quantitative gait analysis is a powerful tool for the assessment of a number of physical and cognitive conditions. Unfortunately, the costs involved in providing in-lab 3D kinematic analysis to all ...

Non-invasive breathing rate detection using a very low power ultra-wide-band radar

In this paper we present a novel method for remote breathing detection based on ultra-wide-band (UWB) radar. This is a method that does not require any wearable sensors, making it more comfortable and convenient for users. Furthermore, because of the wall penetrating characteristics of the transmitted signal, our system is useful in emergency situations such as monitoring people who may be trapped under earthquake rubble. For our investigation we used a Novelda UWB radar that provides high processing speed and low power consumption. In this paper we present two new convolution-based methods to extract breathing rate information from the received radar signal. This method was tested on several people who were monitored while laying down on a bed. The subjects position and breathing rate were calculated. Experimental results including 20 different subjects are provided, showing that this is a viable method for monitoring breathing rate using a low-power UWB radar.

Stream data cleaning for dynamic line rating application

This research was partially funded by Spanish Government under Spanish R+D initiative with reference ENE2013-42720-R and RETOS RTC-2015-3795-3.

Bayesian Network representation of meaningful patterns in electricity distribution grids

The diversity of components in electricity distribution grids makes it impossible, or at least very expensive, to deploy monitoring and fault diagnostics to every individual element. Therefore, power distribution companies are looking for cheap and reliable approaches that can help them to estimate the condition of their assets and to predict the when and where the faults may occur. In this paper we propose a simplified representation of failure patterns within historical faults database, which facilitates visualization of association rules using Bayesian Networks. Our approach is based on exploring the failure history and detecting correlations between different features available in those records. We show that a small subset of the most interesting rules is enough to obtain a good and sufficiently accurate approximation of the original dataset. A Bayesian Network created from those rules can serve as an easy to understand visualization of the most relevant failure patterns. In addition, by varying the threshold values of support and confidence that we consider interesting, we are able to control the tradeoff between accuracy of the model and its complexity in an intuitive way.

A New Two-Degree-of-Freedom Space Heating Model for Demand Response

In today’s fast changing electric utilities sector demand response (DR) programs are a relatively inexpensive means of reducing peak demand and providing ancillary services. Advancements in embedde ...

Activity monitoring as a tool for person-centered care: Preliminary report

The Person-Centered Care (PCC) paradigm advocates that instead of being the passive target of a medical intervention, patients should play an active part in their care and in the decision-making process, together with clinicians. Although new mobile and wearable technologies have created a new wave of personalized health-related applications, it is still unclear how these technologies can be used in health care institutions in order to support person-centered care. In order to investigate this matter, we undertook a pilot study aimed at determining if and how activity monitoring can support person-centered care routines for patients undergoing total hip replacement surgery. This is a preliminary report describing the methodology, preliminary results, and some practical challenges. We present here an orientation-invariant, accelerometer-based activity monitoring method, especially designed to address the requirements of monitoring in-patients in a real clinical setting. We also present and discuss some practical issues related to complying with hospital requirements and collaborating with hospital staff.