Kvantitativna analiza pokreta u rehabilitaciji neuroloških poremećaja korišćenjem vizuelnih i nosivih senzora.

Neuroloska oboljenja, kao sto su Parkinsonova bolest i slog, dovode do ozbiljnih motornih poremecaja, smanjuju kvalitet zivota pacijenata i mogu da uzrokuju smrt. Rana dijagnoza i adekvatno lecenje su krucijalni faktori za drzanje bolesti pod kontrolom, kako bi se omogucio normalan svakodnevni zivot pacijenata. Lecenje neurolo skih bolesti obicno ukljucuje rehabilitacionu terapiju i terapiju lekovima, koje se prilagodavaju u skladu sa stanjem pacijenta tokom vremena. Tradicionalne tehnike evaluacije u dijagnozi i monitoringu neuroloskih bolesti oslanjaju se na klinicke evaluacione alate, tacnije specijalno dizajnirane klinicke testove i skale. Medutim, iako su korisne i najcesce koriscene, klinicke skale su sklone subjektivnim ocenama i nepreciznoj interpretaciji performanse pacijenta...Neurological disorders, such as Parkinson's disease (PD) and stroke, lead to serious motor disabilities, decrease the patients' quality of life and can cause the mortality. Early diagnosis and adequate disease treatment are thus crucial factors towards keeping the disease under control in order to enable the normal every-day life of patients. The treatment of neurological disorders usually includes the rehabilitation therapy and drug treatment, that are adapted based on the evaluation of the patient state over time. Conventional evaluation techniques for diagnosis and monitoring in neurological disorders rely on the clinical assessment tools i.e. specially designed clinical tests and scales. However, although benecial and commonly used, those scales are descriptive (qualitative), primarily intended to be carried out by a trained neurologist, and are prone to subjective rating and imprecise interpretation of patient's performance..

Using Wavelets for Gait and Arm Swing Analysis

The human walking pattern can be affected by different factors such as accidents, transplants, or diseases, like Parkinson’s disease, which affects motor and mental functions. In motor terms, this disease can generate alterations such as tremors, festination, rigidity, unbalance, slowness, and freezing of gait. Additionally, it is estimated that for the year 2040, the number of people with Parkinson’s in the world will be between 12.9 and 14.2 million people. These alarming figures make Parkinson’s disease an important focus of attention. In this chapter, we present contributions that suggest wavelet techniques as a useful tool to perform a gait and arm swing analysis; this represents an important approximation that can contribute to describe and differentiate people with Parkinson’s disease in early stages of the disease

Validity and sensitivity of instrumented postural and gait assessment using low-cost devices in Parkinson's disease

[EN] Background Accurate assessment of balance and gait is necessary to monitor the clinical progress of Parkinson's disease (PD). Conventional clinical scales can be biased and have limited accuracy. Novel interactive devices are potentially useful to detect subtle posture or gait-related impairments. Methods Posturographic and single and dual-task gait assessments were performed to 54 individuals with PD and 43 healthy controls with the Wii Balance Board and the Kinect v2 and the, respectively. Individuals with PD were also assessed with the Tinetti Performance Oriented Mobility Assessment, the Functional Gait Assessment and the 10-m Walking Test. The influence of demographic and clinical variables on the performance in the instrumented posturographic and gait tests, the sensitivity of these tests to the clinical condition and phenotypes, and their convergent validity with clinical scales were investigated. Results Individuals with PD in H&Y I and I.5 stages showed similar performance to controls. The greatest differences in posture and gait were found between subjects in H&Y II.5 and H&Y I-I.5 stage, as well as controls. Dual-tasking enhanced the differences among all groups in gait parameters. Akinetic/rigid phenotype showed worse postural control and gait than other phenotypes. High significant correlations were found between the limits of stability and most of gait parameters with the clinical scales. Conclusions Low-cost devices showed potential to objectively quantify posture and gait in established PD (H&Y >= II). Dual-tasking gait evaluation was more sensitive to detect differences among PD stages and compared to controls than free gait. Gait and posture were more impaired in akinetic/rigid PD.This study has been funded by project VALORA, Grant 201701-10 of the Fundacio la Marato de la TV3 (Barcelona, Spain) and the European Union through the Operational Program of the European Regional Development Fund (ERDF) of the Valencian Community 2014-2020 (IDIFEDER/2018/029) to RL, and Alter Laboratories SA to PP.Álvarez, I.; Latorre, J.; Aguilar, M.; Pastor, P.; Llorens Rodríguez, R. (2020). Validity and sensitivity of instrumented postural and gait assessment using low-cost devices in Parkinson's disease. Journal of NeuroEngineering and Rehabilitation. 17(1):1-10. https://doi.org/10.1186/s12984-020-00770-7S110171Nussbaum RL, Ellis CE. Alzheimer’s Disease and Parkinson’s Disease. N Engl J Med. 2003;13:56–64.Hass CJ, Malczak P, Nocera J, Stegemöller EL, Shukala A, Malaty I, et al. Quantitative normative Gait data in a large cohort of ambulatory persons with parkinson’s disease. PLoS ONE. 2012;2:12.Hass CJ, Bishop M, Moscovich M, Stegemöller EL, Skinner J, Malaty IA, et al. Defining the clinically meaningful difference in gait speed in persons with Parkinson disease. J Neurol Phys Ther. 2014;38:233–8.Koh S, Park K, Lee D. Gait analysis in patients with Parkinson ’ s disease: relationship to clinical features and freezing. J Mov Disord. 2008;1:6.Nanhoe-Mahabier W, Snijders AH, Delval A, Weerdesteyn V, Duysens J, Overeem S, et al. Walking patterns in Parkinson’s disease with and without freezing of gait. Neuroscience. 2011;182:217–24. https://doi.org/10.1016/j.neuroscience.2011.02.061.Raffegeau TE, Krehbiel LM, Kang N, Thijs FJ, Altmann LJP, Cauraugh JH, et al. A meta-analysis: Parkinson’s disease and dual-task walking. Park Relat Disord. 2019;62:28–35.Panyakaew P, Bhidayasiri R. The spectrum of preclinical gait disorders in early Parkinson’s disease: Subclinical gait abnormalities and compensatory mechanisms revealed with dual tasking. J Neural Transm. 2013;120:1665–72.Bloem BR, Marinus J, Almeida Q, Dibble L, Nieuwboer A, Post B, et al. Measurement instruments to assess posture, gait, and balance in Parkinson’s disease: Critique and recommendations. Mov Disord. 2016;31:1342–55.Delval A, Snijders AH, Weerdesteyn V, Duysens JE, Defebvre L, Giladi N, et al. Objective detection of subtle freezing of gait episodes in Parkinson’s disease. Mov Disord. 2010;25:1684–93. https://doi.org/10.1002/mds.23159.Verghese J, Holtzer R, Lipton RB, Wang C. Quantitative gait markers and incident fall risk in older adults. J Gerontol Ser A Biol Sci Med Sci. 2009;64A:896–901.Schlachetzki JCM, Barth J, Marxreiter F, Gossler J, Kohl Z, Reinfelder S, et al. Wearable sensors objectively measure gait parameters in Parkinson’s disease. PLoS ONE. 2017;12:1–18.Godinho C, Domingos J, Cunha G, Santos AT, Fernandes RM, Abreu D, et al. A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease. J Neuroeng Rehabil. 2016;13:1–10. https://doi.org/10.1186/s12984-016-0136-7.Micó-Amigo ME, Kingma I, Faber GS, Kunikoshi A, van Uem JMT, van Lummel RC, et al. Is the assessment of 5 meters of gait with a single body-fixed-sensor enough to recognize idiopathic Parkinson’s disease-associated gait? Ann Biomed Eng. 2017;45:1266–78.Rovini E, Maremmani C, Cavallo F. How wearable sensors can support parkinson’s disease diagnosis and treatment: a systematic review. Front Neurosci. 2017;9:12.Chen S, Lach J, Lo B, Yang GZ. Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review. IEEE J Biomed Health Inform. 2016;9:1521–37.Díaz S, Stephenson JB, Labrador MA. Use of wearable sensor technology in gait, balance, and range of motion analysis. Appl Sci. 2020;10(1):234.Park DS, Lee G. Validity and reliability of balance assessment software using the Nintendo Wii balance board: usability and validation. J Neuroeng Rehabil. 2014;11:99.Holmes JD, Jenkins ME, Johnson AM, Hunt MA, Clark RA. Validity of the Nintendo Wii balance board for the assessment of standing balance in Parkinson’s disease. Clin Rehabil. 2013;27:361–6.Llorens R, Latorre J, Noé E, Keshner EA. Posturography using the Wii Balance BoardTM. A feasibility study with healthy adults and adults post-stroke. Gait Posture. 2016;43:228–32.Bower KJ, McGinley JL, Miller KJ, Clark RA. Instrumented static and dynamic balance assessment after stroke using Wii Balance Boards: Reliability and association with clinical tests. PLoS ONE. 2014;9:32321.Eltoukhy M, Kuenze C, Andersen MS, Oh J, Signorile J. Prediction of ground reaction forces for Parkinson’s disease patients using a kinect-driven musculoskeletal gait analysis model. Med Eng Phys. 2017;50:75–82.Eltoukhy M, Kuenze C, Oh J, Jacopetti M, Wooten S, Signorile J. Microsoft Kinect can distinguish differences in over-ground gait between older persons with and without Parkinson’s disease. Med Eng Phys. 2017;44:1–7.Dolatabadi E, Taati B, Mihailidis A. Concurrent validity of the Microsoft Kinect for Windows v2 for measuring spatiotemporal gait parameters. Med Eng Phys. 2016;38:952–8.Mentiplay BF, Perraton LG, Bower KJ, Pua YH, McGaw R, Heywood S, et al. Gait assessment using the Microsoft Xbox One Kinect: Concurrent validity and inter-day reliability of spatiotemporal and kinematic variables. J Biomech. 2015;48:2166–70.Cao Y, Li BZ, Li QN, Xie JD, Cao BZ, Yu SY. Kinect-based gait analyses of patients with Parkinson’s disease, patients with stroke with hemiplegia, and healthy adults. CNS Neurosci Ther. 2017;9:447–9.Galna B, Barry G, Jackson D, Mhiripiri D, Olivier P, Rochester L. Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson’s disease. Gait Posture. 2014;39:1062–8.Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry. 1992;55:181–4.Goetz CG, Poewe W, Rascol O, Sampaio C, Stebbins GT, Counsell C, et al. Movement Disorder Society Task Force report on the Hoehn and Yahr staging scale: status and recommendations. Mov Disord. 2004;19:1020–8.Escribano-Aparicio MV, Pérez-Dively M, García-García FJ, Pérez-Martín A, Romero L, Ferrer G, et al. Validación del MMSE de Folstein en una población española de bajo nivel educativo1. Rev Esp Geriatr Gerontol. 1999;34:319–26.Latorre J, Colomer C, Alcañiz M, Llorens R. Gait analysis with the Kinect v2: Normative study with healthy individuals and comprehensive study of its sensitivity, validity, and reliability in individuals with stroke. J Neuroeng Rehabil. 2019;16:12.Eltoukhy M, Oh J, Kuenze C, Signorile J. Improved kinect-based spatiotemporal and kinematic treadmill gait assessment. Gait Posture. 2017;51:77–83. https://doi.org/10.1016/j.gaitpost.2016.10.001.Rajput AH, Voll A, Rajput ML, Robinson CA, Rajput A. Course in parkinson disease subtypes: a 39-year clinicopathologic study. Neurology. 2009;73:206–12.Rajput AH, Sitte HH, Rajput A, Fenton ME, Pifl C, Hornykiewicz O. Globus pallidus dopamine and Parkinson motor subtypes: Clinical and brain biochemical correlation. Neurology. 2008;70(16 Pt 2):1403–10.Fahn S. Unified Parkinson’s disease rating scale. Recent Dev Park Dis. 1987;2:153–64.Kaufer DI, Cummings JL, Ketchel P, Smith V, MacMillan A, Shelley T, et al. Validation of the NPI-Q, a Brief Clinical Form of the Neuropsychiatric Inventory. J Neuropsychiatry Clin Neurosci. 2000;12:233–9. https://doi.org/10.1176/jnp.12.2.233.Chaudhuri KR, Martinez-Martin P, Brown RG, Sethi K, Stocchi F, Odin P, et al. The metric properties of a novel non-motor symptoms scale for Parkinson’s disease: results from an international pilot study. Mov Disord. 2007;22:1901–11.Christenson GA, Faber RJ, De Zwaan M, Raymond NC, Specker SM, Ekern MD, et al. Compulsive buying: descriptive characteristics and psychiatric comorbidity. J Clin Psychiatry. 1994;55:5–11.Peto V, Jenkinson C, Fitzpatrick R, Greenhall R. The development and validation of a short measure of functioning and well being for individuals with Parkinson’s disease. Qual life Res. 1995;4:241–8.Lang JT, Kassan TO, Devaney LL, Colon-Semenza C, Joseph MF. Test-retest reliability and minimal detectable change for the 10-meter walk test in older adults with Parkinson’s disease. J Geriatr Phys Ther. 2016;39:165–70.Woodhull-McNeal AP. Changes in posture and balance with age. Aging Clin Exp Res. 1992;4:219–25.Terrier P, Reynard F. Effect of age on the variability and stability of gait: a cross-sectional treadmill study in healthy individuals between 20 and 69 years of age. Gait Posture. 2015;41:170–4.Bohannon RW, Williams AA. Normal walking speed: A descriptive meta-analysis. Physiotherapy. 2011;97:182–9.Elbaz A, Artaud F, Dugravot A, Tzourio C, Singh-Manoux A. The gait speed advantage of taller stature is lost with age. Sci Rep. 2018;8:12.Laroche DP, Marques NR, Shumila HN, Logan CR, Laurent RS, Goncąlves M. Excess body weight and gait influence energy cost of walking in older adults. Med Sci Sports Exerc. 2015;47:1017–25.Stylianou AP, McVey MA, Lyons KE, Pahwa R, Luchies CW. Postural sway in patients with mild to moderate parkinson’s disease. Int J Neurosci. 2011;121:614–21.Mancini M, Horak FB, Zampieri C, Carlson-Kuhta P, Nutt JG, Chiari L. Trunk accelerometry reveals postural instability in untreated Parkinson’s disease. Park Relat Disord. 2011;17:557–62.Morris M, Iansek R, Smithson F, Huxham F. Postural instability in Parkinson’s disease: a comparison with and without a concurrent task. Gait Posture. 2000;12:205–16.Schoneburg B, Mancini M, Horak F, Nutt JG. Framework for understanding balance dysfunction in Parkinson’s disease. Mov Disord. 2013;89:1474–82.Doná F, Aquino CC, Gazzola JM, Borges V, Silva SMCA, Ganança FF, et al. Changes in postural control in patients with Parkinson’s disease: a posturographic study. Physiother. 2016;102:272–9.Rossi M, Soto A, Santos S, Sesar A, Labella T. A Prospective Study of Alterations in Balance among Patients with Parkinson’s Protocol of the Postural Evaluation. Eur Neurol. 2009;11:171–6.Ganesan M, Kumar P, Gupta A, Sathyaprabha TN. Dynamic posturography in evaluation of balance in patients of Parkinson ’ s disease with normal pull test : Concept of a diagonal pull test q. Park Relat Disord. 2010;16:595–9. https://doi.org/10.1016/j.parkreldis.2010.08.005.Kim SM, Kim DH, Yang Y, Ha SW, Han JH. Gait Patterns in Parkinson’s Disease with or without Cognitive Impairment. Dement Neurocognitive Disord. 2018;17:57.Galletly R, Brauer SG. Does the type of concurrent task affect preferred and cued gait in people with Parkinson’s disease? Aust J Physiother. 2005;51:175–80.O’Shea S, Morris ME, Iansek R. Dual task interference during gait in people with Parkinson disease: effects of motor versus cognitive secondary tasks. Phys Ther. 2002;82:888–97.Penko AL, Streicher MC, Koop MM, Dey T, Rosenfeldt AB, Bazyk AS, et al. Dual-task interference disrupts parkinson’s gait across multiple cognitive domains. Neuroscience. 2018;379:375–82.Yogev-seligmann G, Hausdorff JM, Giladi N. The role of executive function and attention in gait. Mov Disord. 2008;23:329–42.Giladi GYN. The contribution of postural control and bilateral coordination to the impact of dual tasking on gait. Exp Brain Res. 2013;226:81–93.Micó-Amigo ME, Kingma I, Heinzel S, Nussbaum S, Heger T, van Lummel RC, et al. Dual vs single tasking during circular walking: what better reflects progression in Parkinson’s disease? Front Neurol. 2019. https://doi.org/10.3389/fneur.2019.00372/full.Duncan RP, Combs-Miller SA, McNeely ME, Leddy AL, Cavanaugh JT, Dibble LE, et al. Are the average gait speeds during the 10 meter and 6 minute walk tests redundant in Parkinson disease? Gait Posture. 2017;52:178–82.Robles-García V, Corral-Bergantiños Y, Espinosa N, Jácome MA, García-Sancho C, Cudeiro J, et al. Spatiotemporal gait patterns during overt and covert evaluation in patients with Parkinson’s disease and healthy subjects: Is there a Hawthorne effect? J Appl Biomech. 2015;31:189–94.Mirelman A, Bernad-Elazari H, Thaler A, Giladi-Yacobi E, Gurevich T, Gana-Weisz M, et al. Arm swing as a potential new prodromal marker of Parkinson’s disease. Mov Disord. 2016;31:1527–34.Ospina BM, Chaparro JAV, Paredes JDA, Pino YJC, Navarro A, Orozco JL. Objective arm swing analysis in early-stage Parkinson’s disease using an RGB-D Camera (Kinect ®). J Parkinsons Dis. 2018;8:563–70.Song J, Sigward S, Fisher B, Salem GJ. Altered dynamic postural control during step turning in persons with early-stage Parkinson’s disease. Parkinsons Dis. 2012. https://doi.org/10.1155/2012/386962.Hausdorff JM, Cudkowicz ME, Firtion R, Wei JY, Goldberger AL. Gait variability and basal ganglia disorders: Stride-to-stride variations of gait cycle timing in Parkinson’s disease and Huntington’s disease. Mov Disord. 1998;13:428–37.Lamberti P, Armenise S, Castaldo V, De Mari M, Iliceto G, Tronci P, et al. Freezing gait in parkinson’s disease. Eur Neurol. 1997;38:297–301.Thenganatt MA, Jankovic J. Parkinson disease subtypes JAMA Neurol. 2014;71:499–504.Lin J-H, Hsu M-J, Hsu H-W, Wu H-C, Hsieh C-L. Psychometric comparisons of 3 functional ambulation measures for patients with stroke. Stroke. 2010;41:2021–5.McDonough AL, Batavia M, Chen FC, Kwon S, Ziai J. The validity and reliability of the GAITRite system’s measurements: A preliminary evaluation. Arch Phys Med Rehabil. 2001;82:419–25.Greenberg M, Gronley J, Perry J, Lawthwaite R. Concurrent validity of observational gait analysis using the vicon motion analysis system. Gait Posture. 1996;4:167–8

Validation of a Single RGB-D Camera for Gait Assessment of Polyneuropathy Patients

Motion analysis systems based on a single markerless RGB-D camera are more suitable for clinical practice than multi-camera marker-based reference systems. Nevertheless, the validity of RGB-D cameras for motor function assessment in some diseases affecting gait, such as Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP), is yet to be investigated. In this study, the agreement between the Kinect v2 and a reference system for obtaining spatiotemporal and kinematic gait parameters was evaluated in the context of TTR-FAP. 3-D body joint data provided by both systems were acquired from ten TTR-FAP symptomatic patients, while performing ten gait trials. For each gait cycle, we computed several spatiotemporal and kinematic gait parameters. We then determined, for each parameter, the Bland Altman's bias and 95% limits of agreement, as well as the Pearson's and concordance correlation coefficients, between systems. The obtained results show that an affordable, portable and non-invasive system based on an RGB-D camera can accurately obtain most of the studied gait parameters (excellent or good agreement for eleven spatiotemporal and one kinematic). This system can bring more objectivity to motor function assessment of polyneuropathy patients, potentially contributing to an improvement of TTR-FAP treatment and understanding, with great benefits to the patients' quality of life.This research was funded by ERDF – European Regional Development Fund through the Operational Program for Competitiveness and Internationalization - COMPETE 2020, and by national funds through the Porto Hospital Center (CHP) in the context of the scholarship BI.02/2018/UCA/CHP, and through the Portuguese Foundation for Science and Technology (FCT), in the context of scholarship SFRH/BD/110438/2015, and projects UID/CEC/00127/2019, UID/CEC/00127/2013, Incentivo/EEI/UI0127/2014, FCOMP-01-0124-FEDER-028943 and FCOMP-01-0124-FEDER-029673. It was also partially funded by NORTE2020 Integrated Project NanoSTIMA “NORTE-01-0145-FEDER-000016”, and POCI-01-0145-FEDER-028618 (PTDC/CCI-COM/28618/2017) - PERFECT, under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fundinfo:eu-repo/semantics/publishedVersio

Reachable Workspace and Proximal Function Measures for Quantifying Upper Limb Motion.

There are a lack of quantitative measures for clinically assessing upper limb function. Conventional biomechanical performance measures are restricted to specialist labs due to hardware cost and complexity, while the resulting measurements require specialists for analysis. Depth cameras are low cost and portable systems that can track surrogate joint positions. However, these motions may not be biologically consistent, which can result in noisy, inaccurate movements. This paper introduces a rigid body modelling method to enforce biological feasibility of the recovered motions. This method is evaluated on an existing depth camera assessment: the reachable workspace (RW) measure for assessing gross shoulder function. As a rigid body model is used, position estimates of new proximal targets can be added, resulting in a proximal function (PF) measure for assessing a subject's ability to touch specific body landmarks. The accuracy, and repeatability of these measures is assessed on ten asymptomatic subjects, with and without rigid body constraints. This analysis is performed both on a low-cost depth camera system and a gold-standard active motion capture system. The addition of rigid body constraints was found to improve accuracy and concordance of the depth camera system, particularly in lateral reaching movements. Both RW and PF measures were found to be feasible candidates for clinical assessment, with future analysis needed to determine their ability to detect changes within specific patient populations

A systematic review of digital technology to evaluate motor function and disease progression in motor neuron disease

Amyotrophic lateral sclerosis (ALS) is the most common subtype of motor neuron disease (MND). The current gold-standard measure of progression is the ALS Functional Rating Scale—Revised (ALS-FRS(R)), a clinician-administered questionnaire providing a composite score on physical functioning. Technology offers a potential alternative for assessing motor progression in both a clinical and research capacity that is more sensitive to detecting smaller changes in function. We reviewed studies evaluating the utility and suitability of these devices to evaluate motor function and disease progression in people with MND (pwMND). We systematically searched Google Scholar, PubMed and EMBASE applying no language or date restrictions. We extracted information on devices used and additional assessments undertaken. Twenty studies, involving 1275 (median 28 and ranging 6–584) pwMND, were included. Sensor type included accelerometers (n = 9), activity monitors (n = 4), smartphone apps (n = 4), gait (n = 3), kinetic sensors (n = 3), electrical impedance myography (n = 1) and dynamometers (n = 2). Seventeen (85%) of studies used the ALS-FRS(R) to evaluate concurrent validity. Participant feedback on device utility was generally positive, where evaluated in 25% of studies. All studies showed initial feasibility, warranting larger longitudinal studies to compare device sensitivity and validity beyond ALS-FRS(R). Risk of bias in the included studies was high, with a large amount of information to determine study quality unclear. Measurement of motor pathology and progression using technology is an emerging, and promising, area of MND research. Further well-powered longitudinal validation studies are needed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00415-022-11312-7

Technological advancements in the analysis of human motion and posture management through digital devices

Technological development of motion and posture analyses is rapidly progressing, especially in rehabilitation settings and sport biomechanics. Consequently, clear discrimination among different measurement systems is required to diversify their use as needed. This review aims to resume the currently used motion and posture analysis systems, clarify and suggest the appropriate approaches suitable for specific cases or contexts. The currently gold standard systems of motion analysis, widely used in clinical settings, present several limitations related to marker placement or long procedure time. Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies, especially outside laboratories. Similarly, new posture analysis techniques are emerging, often driven by the need for fast and non-invasive methods to obtain high-precision results. These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies. The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient. Herein, these devices and their uses are described, providing researchers, clinicians, orthopedics, physical therapists, and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis, therapy, and prevention

Contactless recording of sleep apnea and periodic leg movements by nocturnal 3-D-video and subsequent visual perceptive computing

Contactless measurements during the night by a 3-D-camera are less time-consuming in comparison to polysomnography because they do not require sophisticated wiring. However, it is not clear what might be the diagnostic benefit and accuracy of this technology. We investigated 59 persons simultaneously by polysomnography and 3-D-camera and visual perceptive computing (19 patients with restless legs syndrome (RLS), 21 patients with obstructive sleep apnea (OSA), and 19 healthy volunteers). There was a significant correlation between the apnea hypopnea index (AHI) measured by polysomnography and respiratory events measured with the 3-D-camera in OSA patients (r = 0.823; p < 0.001). The receiver operating characteristic curve yielded a sensitivity of 90% for OSA with a specificity of 71.4%. In RLS patients 72.8% of leg movements confirmed by polysomnography could be detected by 3-D-video and a significant moderate correlation was found between PLM measured by polysomnography and by the 3-D-camera (RLS: r = 0.654; p = 0.004). In total, 95.4% of the sleep epochs were correctly classified by the machine learning approach, but only 32.5% of awake epochs. Further studies should investigate, if this technique might be an alternative to home sleep testing in persons with an increased pre-test probability for OSA