Efficient Semi-structured Queries in Scala using XQuery Shipping

This project proposes a new approach to interact with database systems through programming languages. A formal query language can be integrated within modern programming languages and the semi-structured queries can be evaluated using automatic transformation and query shipping. The focus of this project is on XML queries and Scala programming language. Particularly, this project optimizes the XML-based expressions of Scala using XQuery transformation and Shipping. In this work, Scala sequence comprehensions are extended to cover appropriately the whole functionalities of XQuery FLWOR expressions and XQuery sequence comparisons are introduced in Scala to facilitate query generation. This report presents a formalization of transformation rules between Scala and XQuery languages and describes an Scala implementation. Various use cases are provided to facilitate understanding and employing this newest Scala library

Analysis of gait and coordination for arthroplasty outcome evaluation using body-fixed sensors

The importance of evaluation of an orthopedic operation such as hip or knee arthroplasty has long been recognized. Many definitions of outcome and scoring questionnaires have been used in the past to assess the outcome of joint replacement. However, these assessments are subjective and not accurate enough. In addition, orthopedic surgeons require now more subtle comparisons between potentially efficacious treatments (e.g. two types of prostheses). Therefore, the use of objective instruments that have a better sensitivity and specificity than traditional scoring systems is needed. Gait analysis is one of the most currently used instrumented techniques in this respect. However, a gait analysis system is accessible only in a few specialized laboratories, as it is complex, expensive, need a lot of room space and fixed devices, and not convenient for the patient. In this thesis, we proposed an ambulatory system based on kinematic sensors attached on the lower limbs to overcome the limitations of the previously mentioned techniques. Technically the device is portable, easily mountable, non-invasive, and capable of continuously recording data in long term without hindrance to natural gait. The goal was to provide gait parameters as a new objective method to assess Total Knee Replacement (TKR). New solutions to fusing the data of accelerometers and gyroscopes were proposed to accurately measure lower limbs orientations and joint angles. The methods propose a minimal sensor configuration with one sensor module mounted on each segment. The models consider anatomical aspects and biomechanical constraints. In the proposed techniques, the angles are found without the need for integration, so absolute angles can be obtained which are free from any source of drift. These data were then used to develop a gait analysis system providing spatio-temporal parameters, kinematic curves, and a visualization tool to animate the motion data as synthetic skeletons performing the same actions as the subjects. Moreover, a new algorithm was proposed for assessing and quantification of inter-joint coordination during gait. The coordination model captures the whole dynamics of the lower limbs movements and shows the kinematic synergies at various walking speeds. The model imposes a relationship among lower limb joint angles (hips and knees) to parameterize the dynamics of locomotion for each individual. It provides a coordination score at various walking speeds which is ranged between 0 and 10. An integration of different analysis tools such as Harmonic Analysis, Principal Component Analysis, and Artificial Neural Network helped overcome high-dimensionality, temporal dependence, and non-linear relationships of the gait patterns. In order to show the effectiveness of the proposed methods in outcome evaluation, we have considered a clinical study where the outcomes of two types of knee prostheses were compared. We conducted a randomized controlled study, including 54 patients, to assess TKR outcome between patients with fixed bearing and mobile bearing tibial plates of implants. The patients were tested preoperatively and postoperatively at 6 weeks, 3 months, 6 months, and 1 year. Various statistical analyses were done to compare the outcomes of the two groups. Finally, we provided objective criteria, using ambulatory gait analysis, for assessing functional recovery following TKR procedure. We showed significant difference between the two groups where the standard clinical evaluation was unable to detect such a difference

An Implantable System for Angle Measurement in Prosthetic Knee

In this work we designed and tested an in-vivo measurement system of prosthetic knee joint angles. The system included a small permanent magnet in the femoral part and three magneto resistance sensors placed in the polyethylene part. The sensor configuration was defined based on sensitivity analysis, signal to noise ratio, saturation of sensors and movements constraints. A mapping algorithm was designed to estimate the orientation of the femoral part in sagittal and coronal plane. For validation the prosthesis was placed in a mechanical simulator equipped with reflective markers tracked by optical motion capture

The Use of Wearable Inertial Motion Sensors in Human Lower Limb Biomechanics Studies: A Systematic Review

Wearable motion sensors consisting of accelerometers, gyroscopes and magnetic sensors are readily available nowadays. The small size and low production costs of motion sensors make them a very good tool for human motions analysis. However, data processing and accuracy of the collected data are important issues for research purposes. In this paper, we aim to review the literature related to usage of inertial sensors in human lower limb biomechanics studies. A systematic search was done in the following search engines: ISI Web of Knowledge, Medline, SportDiscus and IEEE Xplore. Thirty nine full papers and conference abstracts with related topics were included in this review. The type of sensor involved, data collection methods, study design, validation methods and its applications were reviewed

Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking

This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody’s (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb’s three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy

A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study

Background The prevalence of self-reported shoulder pain in the UK has been estimated at 16%. This has been linked with significant sleep disturbance. It is possible that this relationship is bidirectional, with both symptoms capable of causing the other. Within the field of sleep monitoring, there is a requirement for a mobile and unobtrusive device capable of monitoring sleep posture and quality. This study investigates the feasibility of a wearable sleep system (WSS) in accurately detecting sleeping posture and physical activity. Methods Sixteen healthy subjects were recruited and fitted with three wearable inertial sensors on the trunk and forearms. Ten participants were entered into a ‘Posture’ protocol; assuming a series of common sleeping postures in a simulated bedroom. Five participants completed an ‘Activity’ protocol, in which a triphasic simulated sleep was performed including awake, sleep and REM phases. A combined sleep posture and activity protocol was then conducted as a ‘Proof of Concept’ model. Data were used to train a posture detection algorithm, and added to activity to predict sleep phase. Classification accuracy of the WSS was measured during the simulations. Results The WSS was found to have an overall accuracy of 99.5% in detection of four major postures, and 92.5% in the detection of eight minor postures. Prediction of sleep phase using activity measurements was accurate in 97.3% of the simulations. The ability of the system to accurately detect both posture and activity enabled the design of a conceptual layout for a user-friendly tablet application. Conclusions The study presents a pervasive wearable sensor platform, which can accurately detect both sleeping posture and activity in non-specialised environments. The extent and accuracy of sleep metrics available advances the current state-of-the-art technology. This has potential diagnostic implications in musculoskeletal pathology and with the addition of alerts may provide therapeutic value in a range of areas including the prevention of pressure sores

Advanced Internet of Things for Personalised Healthcare System: A Survey

As a new revolution of the Internet, Internet of Things (IoT) is rapidly gaining ground as a new research topic in many academic and industrial disciplines, especially in healthcare. Remarkably, due to the rapid proliferation of wearable devices and smartphone, the Internet of Things enabled technology is evolving healthcare from conventional hub based system to more personalised healthcare system (PHS). However, empowering the utility of advanced IoT technology in PHS is still significantly challenging in the area considering many issues, like shortage of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, multi-dimensionality of data generated and high demand for interoperability. In an effect to understand advance of IoT technologies in PHS, this paper will give a systematic review on advanced IoT enabled PHS. It will review the current research of IoT enabled PHS, and key enabling technologies, major IoT enabled applications and successful case studies in healthcare, and finally point out future research trends and challenges

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review.

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare