Effects of nordic walking on gait symmetry in mild Parkinson’s Disease

Individuals with Parkinson’s disease (PD) have gait asymmetries, and exercise therapy may reduce the differences between more and less affected limbs. The Nordic walking (NW) training may contribute to reducing the asymmetry in upper and lower limb movements in people with PD. We compared the effects of 11 weeks of NW aerobic training on asymmetrical variables of gait in subjects with mild PD. Fourteen subjects with idiopathic PD, age: 66.8 ± 9.6 years, and Hoehn and Yard stage of 1.5 points were enrolled. The kinematic analysis was performed pre and post-intervention. Data were collected at two randomized walking speeds (0.28 m·s−1 and 0.83 m·s−1) during five minutes on the treadmill without poles. The more affected and less affected body side symmetries (threshold at 5% between sides) of angular kinematics and spatiotemporal gait parameters were calculated. We used Generalized Estimating Equations with Bonferroni post hoc (α = 0.05). Maximal flexion of the knee (p = 0.007) and maximal abduction of the hip (p = 0.041) were asymmetrical pre and became symmetrical post NW intervention. The differences occurred in the knee was less affected and the hip was more affected. We concluded that 11 weeks of NW training promoted similarities in gait parameters and improved knee and hip angular parameters for PD subjects

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

Locomotion Traces Data Mining for Supporting Frail People with Cognitive Impairment

The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of older people. Thus, this thesis at first focused on providing a systematic literature review of locomotion data mining systems for supporting Neuro-Degenerative Diseases (NDD) diagnosis, identifying locomotion anomaly indicators and movement patterns for discovering low-level locomotion indicators, sensor data acquisition, and processing methods, as well as NDD detection algorithms considering their pros and cons. Then, we investigated the use of sensor data and Deep Learning (DL) to recognize abnormal movement patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduced novel visual feature extraction methods for locomotion data. Our solutions rely on locomotion traces segmentation, image-based extraction of salient features from locomotion segments, and vision-based DL. Furthermore, we proposed a data augmentation strategy to increase the volume of collected data and generalize the solution to different smart-homes with different layouts. We carried out extensive experiments with a large real-world dataset acquired in a smart-home test-bed from older people, including people with cognitive diseases. Experimental comparisons show that our system outperforms state-of-the-art methods

Quantitative gait analysis in mild cognitive impairment, dementia, and cognitively intact individuals: a cross-sectional case–control study

Background: Cognitive age-related decline is linked to dementia development and gait has been proposed to measure the change in brain function. This study aimed to investigate if spatiotemporal gait variables could be used to differentiate between the three cognitive status groups. Methods: Ninety-three older adults were screened and classified into three groups; mild cognitive impairment (MCI) (n = 32), dementia (n = 31), and a cognitively intact (n = 30). Spatiotemporal gait variables were assessed under single- and dual-tasks using an objective platform system. Effects of cognitive status and walking task were analyzed using a two-way ANCOVA. Sub-comparisons for between- and within-group were performed by one-way ANCOVA and Paired t-tests. Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) was used to discriminate between three groups on gait variables. Results: There were significant effects (P <0.05) of cognitive status during both single and dual-task walking in several variables between the MCI and dementia and between dementia and cognitively intact groups, while no difference was seen between the MCI and cognitively intact groups. A large differentiation effect between the groups was found for step length, stride length, and gait speed during both conditions of walking. Conclusions: Spatiotemporal gait variables showed discriminative ability between dementia and cognitively intact groups in both single and dual-tasks. This suggests that gait could potentially be used as a clinical differentiation marker for individuals with cognitive problems

The use of inertial measurement units for the determination of gait spatio-temporal parameters

The aim of this work was to develop a methodology whereby inertial measurement units (IMUs) could be used to obtain accurate and objective gait parameters within typical developed adults (TDA) and Parkinson’s disease (PD). The thesis comprised four studies, the first establishing the validity of the IMU method when measuring the vertical centre of mass (CoM) acceleration, velocity and position versus an optical motion capture system (OMCS) in TDA. The second study addressed the validity of the IMU and inverted pendulum model measurements within PD and also explored the inter-rater reliability of the measurement. In the third study the optimisation of the inverted pendulum model driven by IMU data was explored when comparing to standardised clinical tests within TDA and PD, and the fourth explored a novel phase plot analysis applied to CoM movement to explore gait in more detail. The validity study showed no significant difference for vertical acceleration and position between IMU and OMCS measurements within TDA. Vertical velocity however did show a significant difference, but the error was still less than 2.5%. ICCs for all three parameters ranged from 0.782 to 0.952, indicating an adequate test-retest reliability. Within PD there was no significant difference found for vertical CoM acceleration, velocity and position. ICCs for all three parameters ranged from 0.77 to 0.982. In addition, the reliability calculations found no difference for step time, stride length and walking speed for people with PD. Inter-rater reliability was found not to be different for the same parameters. The optimisation of the correction factor when using the inverted pendulum model showed no significant difference between TDA and PD. Furthermore the correction factor was found not to be related to walking speed. The fourth and final study found that phase plot analysis of variability could be performed on CoM vertical excursion. TDA and PD were shown to have, on average, different characteristics. This thesis demonstrated that CoM motion can be objectively measured within a clinical setting in people with PD by utilizing IMUs. Furthermore, in depth gait variability analysis can be performed by utilizing a phase plot method

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

Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments

Walking patterns can provide important indications of a person’s health status and be beneficial in the early diagnosis of individuals with a potential walking disorder. For appropriate gait analysis, it is critical that natural functional walking characteristics are captured, rather than those experienced in artificial or observed settings. To better understand the extent to which setting influences gait patterns, and particularly whether observation plays a varying role on subjects of different ages, the current study investigates to what extent people walk differentl

Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait

7openopenTrojaniello D; CEREATTI, Andrea; Pelosin E; Avanzino L; Mirelman A; Hausdorff JM; DELLA CROCE, UgoTrojaniello, D; Cereatti, Andrea; Pelosin, E; Avanzino, L; Mirelman, A; Hausdorff, Jm; DELLA CROCE, Ug

An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors

This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment

Diffusion imaging markers of cerebral small vessel disease

Diffusion magnetic resonance imaging (MRI) is widely used as a research tool to assess (subtle) alterations of the cerebral white matter. Measures derived from diffusion MRI appear to be valuable markers for cerebral small vessel disease (SVD). However, SVD is frequently co-occurring with Alzheimer’s disease (AD), and disturbed white matter integrity and altered diffusion measures are considered key findings in both conditions. Yet, the contribution of SVD and AD to diffusion alterations is unclear, which hampers the interpretation of research studies in patients with mixed disease, e.g. memory clinic patients. Study 1 of this thesis aimed to clarify the effect of SVD and AD on diffusion measures by including multiple (memory clinic) samples covering the entire spectrum of SVD, mixed disease, and AD. We calculated diffusion measures from diffusion tensor imaging (DTI) and free water imaging. Within each sample of the disease spectrum, we applied simple regression analyses and multivariable random forest analyses between AD biomarkers (amyloid-beta, tau), conventional MRI markers of SVD, and global diffusion measures. Furthermore, we investigated regional associations between tau on positron emission tomography (PET) and diffusion measures in voxel-wise analyses. Our main findings are that conventional MRI markers of SVD were strongly associated with diffusion measures and showed a higher contribution than AD biomarkers in multivariable analyses across all memory clinic samples. Regional analyses between tau PET and diffusion measures were not significant. We conclude that SVD rather than AD determines diffusion alterations in memory clinic patients. Our findings validate diffusion measures as markers for SVD. Study 2 applied diffusion MRI markers to study gait impairment in SVD. Gait impairment is a commonly reported clinical deficit in SVD patients, but the underlying mechanisms are still debated. The proposed mechanisms include SVD-related white matter alterations resulting in impaired supraspinal locomotor control, cognitive deficits (e.g. planning and execution of movements), and factors independent of SVD, such as age-related instability (e.g. joint wear, sarcopenia) and comorbidities (e.g. neurodegenerative pathology). A reason for the lack of knowledge on gait impairment in SVD is that studies in elderly, sporadic SVD patients are typically confounded by effects of normal-aging and age-related comorbidities. Therefore, Study 2 of this thesis aimed to study the effect of pure SVD on gait performance in a relatively young sample of genetically defined SVD patients without age-related confounding. We performed comprehensive gait assessment using an electronic walkway to obtain multiple spatio-temporal gait parameters standardized based on data from healthy controls. Importantly, we tested the association between diffusion MRI markers of SVD-related white matter alterations and gait performance, since (strategic) white matter alterations are discussed as a major cause of gait decline in the elderly. Furthermore, we assessed the relation between cognitive deficits and gait performance. Our main finding is that, despite severe white matter alterations in pure SVD patients, gait performance was relatively preserved. Cognitive deficits in our study participants were not related to gait impairment. Thus, our results query isolated white matter alterations, in the absence of comorbidities, as a main factor of gait impairment in SVD and suggest that their combination with age-related comorbidities and/or normal-aging may play a crucial role in gait decline. In conclusion, diffusion measures are valid MRI markers of SVD-related white matter alterations. They have significant value both in future research on altered white matter and potentially also in the diagnostic work-up of memory clinic patients, to differentiate between vascular and neurodegenerative disease. Researchers may select target populations for clinical trials based on diffusion measures, e.g. to identify patients with a low SVD burden as targets for prevention and early intervention in SVD. Clinicians and researchers should always consider SVD as the origin of diffusion alterations in patients with mixed pathology. The field of application of diffusion measures is wide and may provide new insights into effects of subtle white matter alterations on clinical deficits, as shown in Study 2 on gait impairment in pure SVD. Future studies should investigate measures from advanced diffusion models and diffusion-based brain network analysis, to further elucidate the mechanisms of clinical deficits in SVD patients