Wearable inertial sensors for human movement analysis

Introduction: The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis.Areas covered: Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice.Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine

Evaluation of spinal posture during gait with inertial measurement units

The increasing number of postural disorders emphasizes the central role of the vertebral spine during gait. Indeed, clinicians need an accurate and non-invasive method to evaluate the effectiveness of a rehabilitation program on spinal kinematics. Accordingly, the aim of this work was the use of inertial sensors for the assessment of angles among vertebral segments during gait. The spine was partitioned into five segments and correspondingly five inertial measurement units were positioned. Articulations between two adjacent spine segments were modeled with spherical joints, and the tilt–twist method was adopted to evaluate flexion–extension, lateral bending and axial rotation. In total, 18 young healthy subjects (9 males and 9 females) walked barefoot in three different conditions. The spinal posture during gait was efficiently evaluated considering the patterns of planar angles of each spine segment. Some statistically significant differences highlighted the influence of gender, speed and imposed cadence. The proposed methodology proved the usability of inertial sensors for the assessment of spinal posture and it is expected to efficiently point out trunk compensatory pattern during gait in a clinical context

Gait characterization using wearable inertial sensors in healthy and pathological populations

Gait analysis is emerging as an effective tool to detect an incipient neurodegenerative disease or to monitor its progression. It has been shown that gait disturbances are an early indicator for cognitive impairments and can predict progression to neurodegenerative diseases. Furthermore, gait performance is a predictor of fall status, morbidity and mortality. Instrumented gait analysis provides quantitative measures to support the investigation of gait pathologies and the definition of targeted rehabilitation programs. In this framework, technologies such as inertial sensors are well accepted, and increasingly employed, as tools to characterize locomotion patterns and their variability in research settings. The general aim of this thesis is the evaluation, comparison and refinement of methods for gait characterization using magneto-inertial measurement units (MIMUs), in order to contribute to the migration of instrumented gait analysis from state of the art to state of the science (i.e.: from research towards its application in standard clinical practice). At first, methods for the estimation of spatio-temporal parameters during straight gait were investigated. Such parameters are in fact generally recognized as key metrics for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Although several methods for their estimate have been proposed, few provided a thorough validation. Therefore an error analysis across different pathologies, multiple clinical centers and large sample size was conducted to further validate a previously presented method (TEADRIP). Results confirmed the applicability and robustness of the TEADRIP method. The combination of good performance, reliability and range of usage indicate that the TEADRIP method can be effectively adopted for gait spatio-temporal parameter estimation in the routine clinical practice. However, while traditionally gait analysis is applied to straight walking, several clinical motor tests include turns between straight gait segments. Furthermore, turning is used to evaluate subjects’ motor ability in more challenging circumstances. The second part of the research therefore headed towards the application of gait analysis on turning, both to segment it (i.e.: distinguish turns and straight walking bouts) and to specifically characterize it. Methods for turn identification based on a single MIMU attached to the trunk were implemented and their performance across pathological populations was evaluated. Focusing on Parkinson’s Disease (PD) subjects, turn characterization was also addressed in terms of onset and duration, using MIMUs positioned both on the trunk and on the ankles. Results showed that in PD population turn characterization with the sensors at the ankles lacks of precision, but that a single MIMU positioned on the low back is functional for turn identification. The development and validation of the methods considered in these works allowed for their application to clinical studies, in particular supporting the spatio-temporal parameters analysis in a PD treatment assessment and the investigation of turning characteristic in PD subjects with Freezing of Gait. In the first application, comparing the pre and post parameters it was possible to objectively determine the effectiveness of a rehabilitation treatment. In the second application, quantitative measures confirmed that in PD subjects with Freezing of Gait turning 360° in place is further compromised (and requires additional cognitive effort) compared to turning 180° while walking

Sensitivity to gait improvement after levodopa intake in Parkinson’s disease: A comparison study among synthetic kinematic indices

The synthetic indices are widely used to describe balance and stability during gait. Some of these are employed to describe the gait features in Parkinson's disease (PD). However, the results are sometimes inconsistent, and the same indices are rarely used to compare the individuals affected by PD before and after levodopa intake (OFF and ON condition, respectively). Our aim was to investigate which synthetic measure among Harmonic Ratio, Jerk Ratio, Golden Ratio and Trunk Displacement Index is representative of gait stability and harmony, and which of these are more sensitive to the variations between OFF and ON condition. We found that all indices, except the Jerk Ratio, significantly improve after levodopa. Only the improvement of the Trunk Displacement Index showed a direct correlation with the motor improvement measured through the clinical scale UPDRS-III (Unified Parkinson's Disease Rating Scale-part III). In conclusion, we suggest that the synthetic indices can be useful to detect motor changes induced by, but not all of them clearly correlate with the clinical changes achieved with the levodopa administration. In our analysis, only the Trunk Displacement Index was able to show a clear relationship with the PD clinical motor improvement

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced

Computation of spatio-temporal parameters in level walking using a single inertial system in lean and obese adolescents

In recent years, the availability of low-cost equipment capable of recording kinematic data during walking has facilitated the outdoor assessment of gait parameters, thus overcoming the limitations of three-dimensional instrumented gait analysis (3D-GA). The aim of this study is twofold: firstly, to investigate whether a single sensor on the lower trunk could provide valid spatio-temporal parameters in level walking in normal-weight and obese adolescents compared to instrumented gait analysis (GA); secondly, to investigate whether the inertial sensor is capable of capturing the spatio-temporal features of obese adolescent gait. These were assessed in 10 obese and 8 non-obese adolescents using both a single inertial sensor on the lower trunk and an optoelectronic system. The parameters obtained were not statistically different in either normal-weight or obese participants between the two methods. Obese adolescents walked with longer stance and double support phase compared to normal-weight participants. The results showed that the inertial system is a valid means of evaluating spatio-temporal parameters in obese individuals

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions

Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions

Quantitative Characterization of Motor Control during Gait in Dravet Syndrome Using Wearable Sensors: A Preliminary Study

: Dravet syndrome (DS) is a rare and severe form of genetic epilepsy characterized by cognitive and behavioural impairments and progressive gait deterioration. The characterization of gait parameters in DS needs efficient, non-invasive quantification. The aim of the present study is to apply nonlinear indexes calculated from inertial measurements to describe the dynamics of DS gait. Twenty participants (7 M, age 9-33 years) diagnosed with DS were enrolled. Three wearable inertial measurement units (OPAL, Apdm, Portland, OR, USA; Miniwave, Cometa s.r.l., Italy) were attached to the lower back and ankles and 3D acceleration and angular velocity were acquired while participants walked back and forth along a straight path. Segmental kinematics were acquired by means of stereophotogrammetry (SMART, BTS). Community functioning data were collected using the functional independence measure (FIM). Mean velocity and step width were calculated from stereophotogrammetric data; fundamental frequency, harmonic ratio, recurrence quantification analysis, and multiscale entropy (τ = 1...6) indexes along anteroposterior (AP), mediolateral (ML), and vertical (V) axes were calculated from trunk acceleration. Results were compared to a reference age-matched control group (112 subjects, 6-25 years old). All nonlinear indexes show a disruption of the cyclic pattern of the centre of mass in the sagittal plane, quantitatively supporting the clinical observation of ataxic gait. Indexes in the ML direction were less altered, suggesting the efficacy of the compensatory strategy (widening the base of support). Nonlinear indexes correlated significantly with functional scores (i.e., FIM and speed), confirming their effectiveness in capturing clinically meaningful biomarkers of gait

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions

Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors).Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity.Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions

Inertial Measurement Unit-Based Gait Event Detection in Healthy and Neurological Cohorts: A Walk in the Dark

A deep learning (DL)-based network is developed to determine gait events from IMU data from a shank- or foot-worn device. The DL network takes as input the raw IMU data and predicts for each time step the probability that it corresponds to an initial or final contact. The algorithm is validated for walking at different self-selected speeds across multiple neurological diseases and both in clinical research settings and the habitual environment. The algorithms shows a high detection rate for initial and final contacts, and a small time error when compared to reference events obtained with an optical motion capture system or pressure insoles. Based on the excellent performance, it is concluded that the DL algorithm is well suited for continuous long-term monitoring of gait in the habitual environment