The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications ambulatory monitoring pervasive computing systems; here, some quantitative analysis human motion its automatic classification the main computational tasks to be pursued. In this paper, we discuss how physical activity can classified using accelerometers, with a major emphasis devoted algorithms employed for purpose. particular, motivate our current...

Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based monitors that collect raw data. The goal is to increase wear time by asking subjects on wrist instead of hip, then use information in signal improve type intensity estimation. purposes this work was obtain an algorithm process ankle data classify behavior into four broad classes: ambulation, cycling, sedentary, other activities.

Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, objective this work is to propose and validate general probabilistic modeling approach classification different pathological gaits. Specifically, presented methodology was tested on data recorded two populations (Huntington’s disease post-stroke subjects) healthy elderly controls using from inertial measurement units placed at shank waist. By extracting...

Study Objectives:To longitudinally examine sleep patterns, habits, and parent-reported problems during the fi rst year of life.Methods: Seven hundred four parent/child pairs participated in a longitudinal cohort study.Structured interview recording general demographic data, feeding intercurrent diseases, family history, parental evaluation infant's carried out at 1, 3, 6, 9, 12 months Results: Nocturnal, daytime, total duration showed high inter-individual variability life associated with...

In this paper, we present an approach to the online implementation of a gait event detector based on machine learning algorithms. Gait events were detected using uniaxial gyro that measured foot instep angular velocity in sagittal plane feed four-state left-right hidden Markov model (HMM). The short-time Viterbi algorithm was used overcome limitation standard algorithm, which does not allow decoding state sequences. Supervised HMM structure and validation with leave-one-subject-out method...

In this paper, we describe an application of hidden Markov models (HMMs) to the problem time-locating specific events in normal gait movement patterns. The use HMMs paper is mainly related opportunity they offer segment data collected at different walking speeds and inclinations surface. A simple four-state left-right HMM trained on a dataset signals from mono-axial gyro during treadmill trials performed speed incline values. mounted foot instep, with its sensitivity axis oriented...

This paper investigates a fall detection system based on the integration of an inertial measurement unit with barometric altimeter (BIMU). The vertical motion body part BIMU was attached to monitored on-line using method that delivered drift-free estimates velocity and height change from floor. experimental study included activities daily living seven types falls five types, simulated by cohort 25 young healthy adults. downward thresholded at 1.38 m/s, yielding 80% sensitivity (SE), 100%...

Purpose State-of-the-art methods for recognizing human activity using raw data from body-worn accelerometers have primarily been validated with collected adults. This study applies a previously available method classification wrist or ankle accelerometer to sets both adults and youth. Methods An algorithm detecting wrist-worn accelerometers, originally developed 33 adults, is tested on set of 20 youth (age, 13 ± 1.3 yr). The also extended by adding new features required improve performance...

Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that useful for automatically inferring physical activity which human subject involved, beside role feeding biomechanical parameters estimators. Automatic classification activities highly attractive pervasive computing systems, whereas contextual awareness may ease human-machine interaction, and biomedicine, wearable sensor systems proposed long-term monitoring. This...

Abstract Objective . Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis are among major challenges in applied neuroscience bioengineering. Usually, information from electrical activity of residual forearm muscles (i.e. electromyogram, EMG) is used to different functions a prosthesis. Noteworthy, EMG patterns at onset contraction (transient phase) have shown contain predictive about upcoming grasps. However, this estimation grasp...

Patients with severe acquired brain injury and prolonged disorders of consciousness (pDoC) are characterized by high clinical complexity risk to develop medical complications. The present multi-center longitudinal study aimed at investigating the impact complications on prediction outcome means machine learning models. pDoC were consecutively enrolled admission in 23 intensive neurorehabilitation units (IRU) followed-up 6 months from onset via Glasgow Outcome Scale-Extended (GOSE)....

Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading difficulties in daily life increased fall risk. Frequent assessment adaptability is therefore essential for monitoring the evolution these impairments and/or long-term effects rehabilitation. The modified index (mDGI) a validated clinical test specifically devoted evaluating facets settings under physiotherapist's supervision. need environment,...

Thermosensitive inks are considered an attractive option for the 3D bioprinting of different tissue types, yet comprehensive information on their reliability, preparation, and properties remains lacking. This paper addresses this gap by presenting a twofold aim: firstly, characterizing rheology, printing aspects two that have demonstrated success in skeletal muscle engineering both vitro vivo. The first ink is composed fibrinogen, gelatin, hyaluronic acid, glycerol, while second sacrificial...

The ambulatory monitoring of human movement can provide valuable information regarding the degree functional ability and general level activity individuals. Since walking is a basic everyday movement, automatic step detection or counting very important in developing systems. This paper concerned with development preliminary validation counter (SC) designed to operate also conditions slow intermittent ambulation. SC was based on processing accelerometer data measured by Gear 2 smartwatch...

Intersubject variability in accelerometer-based activity recognition may significantly affect classification accuracy, limiting a reliable extension of methods to new users. In this paper, we propose an approach for personalizing rules single person. We demonstrate that the method improves detection from wrist-worn accelerometer data on four-class problem interest exercise science community, where classes are ambulation, cycling, sedentary, and other. extend previously published based...

Although inertial and magnetic wearable sensors are promising tools to develop novel technologies for human motion capture, their diffusion is being limited by fair accuracy. In indoor applications, most of the inaccuracy comes from disturbances contained in magnetometer data. Besides, non-technicians might easily fail properly calibrating magnetometers. Hence, a magnetometer-free capture system was developed this work, attempt increase accuracy usability. Innovative strategies, invisible...

Rehabilitation treatments and services are essential for the recovery of post-stroke patients' functions; however, increasing number available therapies lack consensus among outcome measures compromises possibility to determine an appropriate level evidence. Machine learning techniques prognostic applications offer accurate interpretable predictions, supporting clinical decision personalised treatment. The aim this study is develop cross-validate predictive models functional prognosis...