Inclination Measurement of Human Movement Using a 3-D Accelerometer With Autocalibration

In the medical field, accelerometers are often used for measuring inclination of body segments and activity of daily living (ADL) because they are small and require little power. A drawback of using accelerometers is the poor quality of inclination estimate for movements with large accelerations. This paper describes the design and performance of a Kalman filter to estimate inclination from the signals of a triaxial accelerometer. This design is based on assumptions concerning the frequency content of the acceleration of the movement that is measured, the knowledge that the magnitude of the gravity is 1 g and taking into account a fluctuating sensor offset. It is shown that for measuring trunk and pelvis inclination during the functional three-dimensional activity of stacking crates, the inclination error that is made is approximately 2/spl deg/ root-mean square. This is nearly twice as accurate as compared to current methods based on low-pass filtering of accelerometer signals

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Developing Predictive Models for Upper Extremity Post–Stroke Motion Quality Estimation Using Decision Trees and Bagging Forest

Stroke is one of the leading causes of long–term disability. Approximately twothirds of stroke survivors require long-term rehabilitation, which suggests the importance of understanding the post-stroke recovery process during his activities of daily living. This problem is formulated as quantifying and estimating the poststroke movement quality in real world settings. To address this need, we have developed an approach that quantifies physical activities and can evaluate the performance quality. Wearable accelerometer and gyroscope are used to measure the upper extremity motions and to develop a mathematical framework to objectively relates sensors’ data to clinical performance indices. In this article we employ two machine learning classification methods, Bootstrap Aggregating (Bagging) Forest and Decision Tree (DT), to relate the post-stroke kinematic data to quality of the corresponding motion. We then compare the accuracy of the resulted two prediction models using cross-validation approaches. Our findings indicate that Bagging forest approach is superior to the computationally simpler DTs for unstable data sets including those derived from stroke survivors in this project

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Pulse transit time measured by photoplethysmography improves the accuracy of heart rate as a surrogate measure of cardiac output, stroke volume and oxygen uptake in response to graded exercise

Heart rate (HR) is a valuable and widespread measure for physical training programs, although its description of conditioning is limited to the cardiac response to exercise. More comprehensive measures of exercise adaptation include cardiac output ((Q) over dot), stroke volume (SV) and oxygen uptake ((V) over dotO(2)), but these physiological parameters can be measured only with cumbersome equipment installed in clinical settings. In this work, we explore the ability of pulse transit time (PTT) to represent a valuable pairing with HR for indirectly estimating (Q) over dot, SV and (V) over dotO(2) non-invasively. PTT was measured as the time interval between the peak of the electrocardiographic (ECG) R-wave and the onset of the photoplethysmography (PPG) waveform at the periphery (i.e. fingertip) with a portable sensor. Fifteen healthy young subjects underwent a graded incremental cycling protocol after which HR and PTT were correlated with (Q) over dot, SV and (V) over dotO(2) using linear mixed models. The addition of PTT significantly improved the modeling of (Q) over dot, SV and (V) over dotO(2) at the individual level (R-1(2) = 0.419 for SV, 0.548 for (Q) over dot, and 0.771 for (V) over dotO(2)) compared to predictive models based solely on HR (R-1(2) = 0.379 for SV, 0.503 for (Q) over dot, and 0.745 for (V) over dotO(2)). While challenges in sensitivity and artifact rejection exist, combining PTT with HR holds potential for development of novel wearable sensors that provide exercise assessment largely superior to HR monitors

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Predicting objectively assessed physical activity from the content and regulation of exercise goals: evidence for a mediational model

Grounded in self-determination theory (Deci & Ryan, 2000), the purpose of this work was to examine effects of the content and motivation of adults’ exercise goals on objectively assessed moderate-to-vigorous physical activity (MVPA). After reporting the content and motivation of their exercise goals, 101 adult participants (Mage = 38.79 years; SD = 11.5) wore an ActiGraph (GT1M) accelerometer for seven days. Accelerometer data were analyzed to provide estimates of engagement in MVPA and bouts of physical activity. Goal content did not directly predict behavioral engagement; however, mediation analysis revealed that goal content predicted behavior via autonomous exercise motivation. Specifically, intrinsic versus extrinsic goals for exercise had a positive indirect effect on average daily MVPA, average daily MVPA accumulated in 10-min bouts and the number of days on which participants performed 30 or more minutes of MVPA through autonomous motivation. These results support a motivational sequence in which intrinsic versus extrinsic exercise goals influence physical activity behavior because such goals are associated with more autonomous forms of exercise motivation

TOWARD ACCELEROMETER RECORDING OF PIT VIPER FORAGING BEHAVIOR IN NATURE: VALIDATION AND CASE STUDY WITH TIMBER RATTLESNAKES (CROTALUS HORRIDUS)

Accelerometer dataloggers are becoming increasingly common in field studies of animal behavior. Among the most difficult study subjects, and consequently, the most ideal candidates for accelerometer applications, are those for which simultaneous observation of key fitness-determining behaviors, such as foraging, across a sample of individuals in the wild is traditionally impossible or restricted to the use of proxy measures. This is the case for many solitary vertebrate predators, such as all pit vipers (Viperidae; Crotalinae). Large-bodied pit vipers are ambush (sit-and-wait) specialists that represent uniquely challenging and intriguing study subjects in predator ecology. Unlike many comparable avian or mammalian terrestrial predators, pit vipers are not as amenable to the external attachment of accelerometers (given their elongate and limbless morphology, and periodic ecdysis). However, pit vipers also present some logistical advantages over other vertebrate predators; as ectothermic, low-energy specialists, they often occur at much higher densities than other apex predators and are therefore logistically appealing for the longitudinal monitoring of large samples of individuals within relatively small areas. Additionally, the most prominent predatory behaviors associated with the foraging cycle of pit vipers (locomotion [active search], stillness [sit-and-wait], striking, and swallowing) are all temporally and biomechanically distinct, lending conceptual feasibility to the use of accelerometers for accurate segmentation of these behaviors. Two recently validated frameworks for accelerometer monitoring of movement and foraging behavior in rattlesnakes (Crotalus spp.) have set the stage for translating an integration of these methods in the field. Chapter one summarizes the transformative use of biologging devices, and accelerometers, specifically, for studying the foraging behavior of predators, while emphasizing the conceptual and logistical feasibility of pit vipers in this context. Chapter two reports on a case study that applies the above-mentioned frameworks for accelerometer recording of movement and foraging behavior in wild-ranging Timber Rattlesnakes (Crotalus horridus) from the lower Piedmont of Georgia, USA

Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis

The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPod’s inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures. The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the “bench to the bedside.” This review only identified a few studies that explored AT’s potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-user’s perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system. With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The study’s analysis of the trunk’s vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a person’s gait cycle, ultimately permitting more clinically relevant gait features to be extracted. Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunk’s anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances. Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookes’ spin-off company ‘Wildknowledge’, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments

Zasada działania i zastosowanie akcelerometrii w ocenie układu ruchu : przegląd narracyjny

Akcelerometria jest stosunkowo młodą, ale obiecującą metodą w dziedzinie badań nad chodem. Bazuje ona na zastosowaniu czujników mierzących przyspieszenie liniowe występujące w danym punkcie materialnym. Celem tego artykułu jest przegląd literatury pod kątem zastosowania tej techniki w ocenie lokomocji człowieka, zalet i wad oraz rzetelności pomiaru. Przeglądnięto prace różnych autorów i porównano ich wyniki. Badania dotyczyły wykrywania faz chodu, obliczania parametrów takich jak prędkość czy długość kroków, oceny równowagi oraz monitorowania aktywności fizycznej. W celu sprawdzenia poprawności zarejestrowanych danych, porównywano je z odczytami systemu VICON, platform dynamometrycznych oraz specjalnych elektronicznych ścieżek. Analiza literatury dostarczyła następujących wniosków. Zaletami akcelerometrii jest niski koszt urządzeń, ich niewielkie rozmiary oraz masa, a także brak ograniczenia pomiaru do wnętrza laboratorium. Wady to przede wszystkim konieczność stosowania kabli, co utrudnia długotrwały monitoring aktywności fizycznej. Metoda jest rzetelna, o ile eksperyment jest prawidłowo zaplanowany i przeprowadzony. Najważniejsze warunki to właściwe umiejscowienie czujników, zapewniające dobre przyleganie do ciała mocowanie, jak najdokładniejsze skoordynowanie osi anatomicznej z osią pomiaru oraz użycie właściwego algorytmu przetwarzania danych. Autorzy większości prac uznają akcelerometrię jako wiarygodną i przydatną metodę do oceny parametrów chodu. Obecnie akcelerometry znajdują zastosowanie głównie przy badaniu wzorca chodu i oceny dysfunkcji, jako czujniki FES u pacjentów z opadającą stopą oraz podczas oceny równowagi oraz monitorowania aktywności fizycznej.Accelerometry is a relatively new but promising method of gait examination. It is based on the usage of sensors which measure linear acceleration at a certain material point. The purpose of this article is to review the literature on the subject from the point of view of applying this technique in assessing human gait, its advantages and shortcomings and the reliability of measurement. Papers by various authors have been reviewed and their results compared. Research concerned detection of the phases and events of gait, calculation of gait parameters such as speed and step length, balance evaluation and the monitoring of physical activity. In order to verify the correctness of the collected data, it was compared with the readings of the VICON system, force platforms and special electronic walkways. An analysis of the literature resulted in the following conclusions: the advantages of accelerometry is the low cost of devices, their small size and mass and measurement which is not limited to the laboratory. The disadvantage is first of all the necessity to use cables, which makes it harder to conduct the long-term monitoring of physical activity. The method is reliable if the experiment is properly planned and carried out. The most important conditions are the proper location of sensors, tight binding to the body, the most accurate alignment of the anatomical axis with the measurement axis and the usage of a proper algorithm for data processing. The authors of the majority of papers consider accelerometry to be a reliable and useful method of analyzing the parameters of gait. At present, accelerometers are used mainly for examining the model of gait and assessing dysfunctions, as sensors in FES assisted walking in patients with dropped foot and during physical activity monitoring