Posts Tagged Acceleration

[Abstract + References] Novel Assessment Measures of Upper-Limb Function in Pre and Poststroke Rehabilitation: A Pilot Study – IEEE Conference Publication

Abstract

Hand function assessment is essential for upper limb rehabilitation of stroke survivors. Conventional acquisition devices have inherent and restrictive difficulties for their clinical usage. Data gloves are limited for applications outside the medical environment, and motion tracking systems setup are time and personnel demanding. We propose a novel instrument designed as a replica of a glass, equipped with an omnidirectional vision system to capture hand images and an inertial measurement unit for movements kinematic data acquisition. Four stroke survivors were invited as volunteers in pre and post-treatment experiments for its evaluating. The exercise of drinking water from a glass was elected for the trails. Before treatment, subjects used their contralesional and ipsilateral hands to perform them. Two main functional features were found in the data analysis. There were differences between limbs in the grasping hand postures, mainly in the index and thumb abduction angle, and in the task timing. After treatment, two volunteers repeated the protocol with their contralesional hands. Changes in the features were observed, index and thumb abduction angles were greater in both cases, and tasks timing were altered in distinct ways. These preliminary results suggest the instrument can be used both in evaluation of hand functional deficit and rehabilitation progress. Improvements and future work are also presented.
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2. C. A. Doman, K. J. Waddell, R. R. Bailey, J. L. Moore, C. E. Lang, “Changes in upper-extremity functional capacity and daily performance during outpatient occupational therapy for people with stroke”, American Journal of Occupational Therapy, vol. 70, no. 3, pp. 7003290040pl-7003290040p11, 2016.

3. B. Brouwer, M. V. Sale, M. A. Nordstrom, “Asymmetry of motor cortex excitability during a simple motor task: relationships with handedness and manual performance”, Experimental Brain Research, vol. 138, no. 4, pp. 467-476, 2001.

4. J. Langan, P. van Donkelaar, “The influence of hand dominance on the response to a constraint-induced therapy program following stroke”, Neurorehabilitation and neural repair, vol. 22, no. 3, pp. 298-304, 2008.

5. H. I. Krebs, M. L. Aisen, B. T. Volpe, N. Hogan, “Quantization of continuous arm movements in humans with brain injury”, Proceedings of the National Academy of Sciences, vol. 96, no. 8, pp. 4645-4649, 1999.

6. B. Fisher, C. Winstein, M. Velicki, “Deficits in compensatory trajectory adjustments after unilateral sensorimotor stroke”, Experimental brain research, vol. 132, no. 3, pp. 328-344, 2000.

7. H. Sugarman, A. Avni, R. Nathan, A. Weisel-Eichler, J. Tiran, “Movement in the ipsilesional hand is segmented following unilateral brain damage”, Brain and cognition, vol. 48, no. 2-3, pp. 579-587, 2002.

8. D. A. Nowak, “The impact of stroke on the performance of grasping: usefulness of kinetic and kinematic motion analysis”, Neuroscience & Biobehavioral Reviews, vol. 32, no. 8, pp. 1439-1450, 2008.

9. M. Coluccini, E. S. Maini, C. Martelloni, G. Sgandurra, G. Cioni, “Kinematic characterization of functional reach to grasp in normal and in motor disabled children”, Gait & posture, vol. 25, no. 4, pp. 493-501, 2007.

10. E. Jaspers, H. Feys, H. Bruyninckx, J. Harlaar, G. Molenaers, K. Desloovere, “Upper limb kinematics: development and reliability of a clinical protocol for children”, Gait & posture, vol. 33, no. 2, pp. 279-285, 2011.

11. D. A. Nowak, J. Hermsdörfer, H. Topka, “Deficits of predictive grip force control during object manipulation in acute stroke”, Journal of neurology, vol. 250, no. 7, pp. 850-860, 2003.

12. R. W. Bohannon, “Adequacy of hand-grip dynamometry for characterizing upper limb strength after stroke”, Isokinetics and exercise science, vol. 12, no. 4, pp. 263-265, 2004.

13. H. Zhou, H. Hu, “Human motion tracking for rehabilitationâĂŤa survey”, Biomedical Signal Processing and Control, vol. 3, no. 1, pp. 1-18, 2008.

14. A. C. P. Rocha, E. Tudella, L. M. Pedro, V. C. R. Appel, L. G. P. da Silva, G. A. d. P. Caurin, “A novel device for grasping assessment during functional tasks: preliminary results”, Frontiers in bioengineering and biotechnology, vol. 4, pp. 16, 2016.

15. E. Taub, G. Uswatte, “Constraint-induced movement therapy: bridging from the primate laboratory to the stroke rehabilitation laboratory”, Journal of Rehabilitation Medicine-Supplements, vol. 41, pp. 34-40, 2003.

16. R. d. N. B. Marques, A. C. Magesto, R. E. Garcia, C. B. d. Oliveira, G. d. S. Matuti, “Efeitos da terapia por contensão induzida nas lesões encefálicas adquiridas”, Fisioterapia Brasil, vol. 17, no. 1, pp. f-30, 2016.

17. E. E. Butler, A. L. Ladd, L. E. LaMont, J. Rose, “Temporal-spatial parameters of the upper limb during a reach & grasp cycle for children”, Gait & posture, vol. 32, no. 3, pp. 301-306, 2010.

18. E. E. Butler, A. L. Ladd, S. A. Louie, L. E. LaMont, W. Wong, J. Rose, “Three-dimensional kinematics of the upper limb during a reach and grasp cycle for children”, Gait & posture, vol. 32, no. 1, pp. 72-77, 2010.

19. L. Gauthier, Structural brain changes produced by different motor therapies after stroke, 2011.

20. L. M. Pedro, G. A. de Paula Caurin, “Kinect evaluation for human body movement analysis”, Biomedical Robotics and Biomechatronics (BioRob) 2012 4th IEEE RAS & EMBS International Conference on, pp. 1856-1861, 2012.

21. A. Hussain, S. Balasubramanian, N. Roach, J. Klein, N. Jarrassé, M. Mace, A. David, S. Guy, E. Burdet, “Sitar: a system for independent task-oriented assessment and rehabilitation”, Journal of Rehabilitation and Assistive Technologies Engineering, vol. 4, pp. 2055668317729637, 2017.

22. L. R. L. Cardoso, M. N. Martelleto, P. M. Aguiar, E. Burdet, G. A. P. Caurin, L. M. Pedro, “Upper limb rehabilitation through bicycle controlling”, 24th International Congress of Mechanical Engineering, 2017.

23. M. N. Martelleto, P. M. Aguiar, E. Burdet, G. A. P. Caurin, R. V. Aroca, L. M. Pedro, “Instrumented module for investigation of contact forces for use in rehabilitation and assessment of bimanual functionalities”, 24th International Congress of Mechanical Engineering, 2017.

 

via Novel Assessment Measures of Upper-Limb Function in Pre and Poststroke Rehabilitation: A Pilot Study – IEEE Conference Publication

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[ARTICLE] Neural predictors of gait stability when walking freely in the real-world – Full Text

Abstract

Background

Gait impairments during real-world locomotion are common in neurological diseases. However, very little is currently known about the neural correlates of walking in the real world and on which regions of the brain are involved in regulating gait stability and performance. As a first step to understanding how neural control of gait may be impaired in neurological conditions such as Parkinson’s disease, we investigated how regional brain activation might predict walking performance in the urban environment and whilst engaging with secondary tasks in healthy subjects.

Methods

We recorded gait characteristics including trunk acceleration and brain activation in 14 healthy young subjects whilst they walked around the university campus freely (single task), while conversing with the experimenter and while texting with their smartphone. Neural spectral power density (PSD) was evaluated in three brain regions of interest, namely the pre-frontal cortex (PFC) and bilateral posterior parietal cortex (right/left PPC). We hypothesized that specific regional neural activation would predict trunk acceleration data obtained during the different walking conditions.

Results

Vertical trunk acceleration was predicted by gait velocity and left PPC theta (4–7 Hz) band PSD in single-task walking (R-squared = 0.725, p = 0.001) and by gait velocity and left PPC alpha (8–12 Hz) band PSD in walking while conversing (R-squared = 0.727, p = 0.001). Medio-lateral trunk acceleration was predicted by left PPC beta (15–25 Hz) band PSD when walking while texting (R-squared = 0.434, p = 0.010).

Conclusions

We suggest that the left PPC may be involved in the processes of sensorimotor integration and gait control during walking in real-world conditions. Frequency-specific coding was operative in different dual tasks and may be developed as biomarkers of gait deficits in neurological conditions during performance of these types of, now commonly undertaken, dual tasks.

Background

Recent developments in mobile technologies enable the design of experiments describing behavioural and neural responses of subjects performing commonly observed tasks in real-world scenarios outside of the experimental lab environment [1]. Such tasks may include artistic performance such as dancing and music playing [2], dealing with stressful situations [3] and evaluating changes in the levels of “excitement”, “engagement” and “frustration” when walking within different city areas [45]. An interesting aspect of these novel experimental approaches is the possibility to correlate brain activity and natural behaviour, in both healthy and neurologically impaired populations [1]. For example, recent evidence has suggested that the pre-frontal cortex (PFC) is involved in multitasking behaviours [678] and that the posterior parietal cortex (PPC) is engaged in motor adaptation during walking in health [91011]. These regions have also been shown to be involved in different attentional [12] and executive function networks [13]. Gait initiation failure (GIF) and freezing of gait (FoG) episodes in freely walking Parkinson’s disease (PD) patients have been correlated with increased neural activity and connectivity between different cortical regions such as occipital, parietal and frontal regions [1415]. Clinically, difficulties in free walking are observed to increase with the severity of PD due to damage in the cortical-striatal locomotor network [16]. Ambulatory abilities of PD patients are impaired by muscular hypertonia and hypokinesia, which induce asymmetries and reduce speed, as well as FoG [17]. PD patients have less control of their posture when standing, walking and compensating for an external perturbation and this may lead to an increased magnitude of postural sway [18]. Specifically, the magnitude of medio-lateral sway was shown to be highly sensitive to postural impairments during both standing and over-ground free walking and this progressed with the severity of PD [1920].

In ths study, we used a smartphone to measure the acceleration root mean square index (RMS) as an indication of the magnitude of movements or sway at the pelvis in any of the three movement directions (i.e., vertical, antero-posterior and medio-lateral) [18212223]. Previous investigations have shown that RMS increases at the level of the pelvis when walking on an insidious surface (i.e., more difficult) compared to smooth conditions, but not at the head [2124]. Normalization procedures have also been developed for RMS data to reliably compare the quality and variability of real-world gait between different populations (healthy young vs. elderly vs. neurologically impaired) and at different gait speeds [22252627].

Whilst RMS has been correlated with age or level/type of neurological impairments, there have been no models of how neural activation can predict gait stability [20]. We hypothesised that in healthy young subjects, neural activity in the PFC and PPC regions would predict gait stability, specifically measured with the acceleration RMS index. To test our hypothesis, we investigated the relationships between neural activity and RMS index during different ambulatory conditions outside the laboratory using real life tasks. We studied three common ambulatory tasks, namely self-paced free walking, walking whilst conversing and walking whilst texting on a smartphone in order to better understand the neural correlates underlying human natural behaviours.[…]

 

Continue —> Neural predictors of gait stability when walking freely in the real-world | Journal of NeuroEngineering and Rehabilitation | Full Text

 

Fig. 1 Mobile Setup for real-world experiments. Brain activity was recorded by a 64 channel EEG Waveguard cap connected to the EEGoPro amplifier placed into a backpack together with a tablet on which the recording software ran. Contact Switches were placed underneath the subject’s heels and connected to a digital input of the MWX8 DataLog analog-to-digital converter fixed at the subject’s hips by an elastic belt. Elastic bands were also placed around the subject’s thighs to make sure cables did not disturb gait performance. A digital button was connected to the converter and pressed by the subject at specific time points. A Samsung Galaxy S4 mini was firmly placed at the subject’s lower back with the elastic belt. Author S.P. gave written informed consent for the usage of this picture

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