Inertial motion capture relies on accurate sensor-to-segment calibration. When two segments are connected by a hinge joint, for example in human knee or finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. Methods for estimating the joint axis using accelerations and angular rates of arbitrary motion have been proposed, but the user must perform sufficiently informative motion in a predefined initial time window to accomplish complete identifiability. Another drawback of state of the art methods is that the user has no way of knowing if the calibration was successful or not. To achieve plug-and-play calibration, it is therefore important that 1) sufficiently informative data can be extracted even if large portions of the data set consist of non-informative motions, and 2) the user knows when the calibration has reached a sufficient level of accuracy. In the current paper, we propose a novel method that achieves both of these goals. The method combines acceleration- and angular rate information and finds a globally optimal estimate of the joint axis. Methods for sample selection, that overcome the limitation of a dedicated initial calibration time window, are proposed. The sample selection allows estimation to be performed using only a small subset of samples from a larger data set as it deselects non-informative and redundant measurements. Finally, an uncertainty quantification method that assures validity of the estimated joint axis parameters, is proposed. Experimental validation of the method is provided using a mechanical joint performing a large range of motions. Angular errors in the order of $2^{\circ }$ were achieved using 125–1000 selected samples. The proposed method is the first truly plug-and-play method that overcome the need for a specific calibration phase and, regardless of the user’s motions, it provides an accurate estimate of the joint axis as soon as possible.

We study dual-task performance with marker-free video recordings of Timed Up-and-Go tests (TUG) and TUG combined with a cognitive/verbal task (TUG dual-task, TUGdt). Can gait parameters be accurately estimated from video-recorded TUG tests by a new semi-automatic method aided by a technique for human 2D pose estimation based on deep learning? Thirty persons aged 60-85 years participated in the study, conducted in a laboratory environment. Data were collected by two synchronous video-cameras and a marker-based optoelectronic motion capture system as gold standard, to evaluate the gait parameters step length (SL), step width (SW), step duration (SD), single-stance duration (SSD) and double-stance duration (DSD). For reliability evaluations, data processing aided by a deep neural network model, involved three raters who conducted three repetitions of identifying anatomical keypoints in recordings of one randomly selected step from each of the participants. Validity was analysed using 95 % confidence intervals (CI) and p-values for method differences and Bland-Altman plots with limits of agreement. Inter- and intra-rater reliability were calculated as intraclass correlation coefficients (ICC) and standard errors of measurement. Smallest detectable change was calculated for inter-rater reliability. Mean ddifferences between video and the motion capture system data for SW, DSD, and SSD were significant (p < 0.001). However, mean differences for all parameters were small (-6.4%-13.0% of motion capture system) indicating good validity. Concerning reliability, almost all 95 % CI of the ICC estimates exceeded 0.90, indicating excellent reliability. Only inter-rater reliability for SW (95 % CI = 0.892;0.973) and one rater's intra-rater reliability for SSD (95 % CI = 0.793;0.951) were lower, but still showed good to excellent reliability. The presented method for extraction of gait parameters from video appears suitable for valid and reliable quantification of gait. This opens up for analyses that may contribute to the knowledge of cognitive-motor interference in dual-task testing.

Objective quantification of the balancing mechanisms in humans is strongly needed in health care of older people, yet is largely missing among current clinical balance assessment methods. Hence, the main goal of this literature review is to identify methods that have the potential to meet that need. We searched in the PubMed and IEEE Xplore databases using predefined criteria, screened 1064 articles, and systematically reviewed and categorized methods from 73 studies that deal with identification of neuromuscular controller models of human upright standing from empirical data. These studies were then analyzed with the particular aim to understand to what degree such methods would be useful solutions for assessing the balance of older individuals aged above 60 years. The 16 studies that included an older subject population were especially examined with this in mind. The majority of the reviewed articles focused on research questions related to the general function of human balance control rather than clinical applicability. Further efforts need to be made to adapt these methods for more accessible and mobile technologies and to ensure that the outcomes are valid for balance assessment of a general older population.

Sensor-to-segment calibration is a crucial step in inertial motion tracking. When two segments are connected by a hinge joint, for example in human knee and finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. There exist methods that identify these coordinates by solving an optimization problem that is based on kinematic joint constraints, which involve either the measured accelerations or the measured angular rates. In the current paper we demonstrate that using only one of these constraints leads to inaccurate estimates at either fast or slow motions. We propose a novel method based on a cost function that combines both constraints. The restrictive assumption of a homogeneous magnetic field is avoided by using only accelerometer and gyroscope readings. To combine the advantages of both sensor types, the residual weights are adjusted automatically based on the estimated signal variances and a nonlinear weighting of the acceleration norm difference. The method is evaluated using real data from nine different motions of an upper limb exoskeleton. Results show that, unlike previous approaches, the proposed method yields accurate joint axis estimation after only five seconds for all fast and slow motions.

This paper deals with a low-complexity time-domain tremor quantification method for individualization of therapies in medical conditions, where tremor is a cardinal symptom. This method produces a tremor severity estimate based on the measurements acquired from a standard sensor platform of a smart phone during a smooth voluntary movement. The estimate is calculated from a data set recorded over tens of seconds but can also be used for unobtrusive tremor monitoring over long time. Besides tremor amplitude, its frequency is also evaluated over time, thus providing the means to distinguish between, e.g., rest and action tremor. No analytical model is assumed for the tremor signal form. The characterization of tremor severity is performed by the steady-state analysis of a Markov chain, whose states correspond to different intervals of tremor amplitude. The utility of the proposed quantification approach is illustrated by clinical data obtained during Deep Brain Stimulation programming sessions. The results are compared with a conventional approach utilizing spectral analysis to demonstrate the benefits of the proposed method.

This document concerns data readiness in the context of machine learning and Natural Language Processing. It describes how an organization may proceed to identify, make available, validate, and prepare data to facilitate automated analysis methods. The contents of the document is based on the practical challenges and frequently asked questions we have encountered in our work as an applied research institute with helping organizations and companies, both in the public and private sectors, to use data in their business processes.