Spatiotemporal parameters of gait are general gait parameters that can enable the simplest form of objective gait evaluation. These parameters tend to change in most locomotor disabilities and serve as guidelines for evaluating the walking ability of a subject. Wearable devices with sensors, such as accelerometers and gyroscopes, have been extensively used not only for gait analysis but also for fall detection and monitoring of physical activity and classification of posture and movement. In gait analysis, wearable technologies are mostly used to extract the spatiotemporal parameters of gait.
This study aimed to validate the application of an inertial measurement unit (IMU) sensor for calculating the spatiotemporal parameters of gait in healthy young, older adults, and stoke patients. Six healthy young adults, 4 older adults, and 10 stroke patients participated in this study; each wore an IMU sensor on the fifth lumbar vertebra (L5) while the sensor was synchronized with a three-dimensional motion capture system. Nineteen gait parameters, including duration, variability, and asymmetry, were calculated from initial contact and final contact event times. The statistical analysis result showed that all three groups has good to excellent agreement for four most commonly reported gait characteristic (stride-, step-, stance-,and swing-duration). Meanwhile, for asymmetry and variability, good agreement between two systems only found on subject with stroke group.
The second aim for this study is to classified the gait patterns of normal and stroke participants by using time- and frequency-domain features obtained from data provided by an Inertial Measurement Unit (IMU) sensor placed on the subject’s lower back (L5). Twenty-three participants were included and divided into two groups: healthy group (young and older adults) and stroke group. A feature selection method comprising statistical analysis and Signal-to-Noise Ratio (SNR) calculation was used to reduce the number of features. The features were used to train four Support Vector Machine (SVM) kernels, and the results were subsequently compared. The quadratic SVM kernel had the highest accuracy (93.46%), as evaluated through cross validation. Moreover, when different datasets were used on model testing, both the quadratic and cubic kernels showed the highest accuracy (96.55%). These results demonstrate the effectiveness of this study’s classification method in distinguishing between normal and stroke gait patterns.

Spatiotemporal parameters of gait are general gait parameters that can enable the simplest form of objective gait evaluation. These parameters tend to change in most locomotor disabilities and serve as guidelines for evaluating the walking ability of a subject. Wearable devices with sensors, such as accelerometers and gyroscopes, have been extensively used not only for gait analysis but also for fall detection and monitoring of physical activity and classification of posture and movement. In gait analysis, wearable technologies are mostly used to extract the spatiotemporal parameters of gait.
This study aimed to validate the application of an inertial measurement unit (IMU) sensor for calculating the spatiotemporal parameters of gait in healthy young, older adults, and stoke patients. Six healthy young adults, 4 older adults, and 10 stroke patients participated in this study; each wore an IMU sensor on the fifth lumbar vertebra (L5) while the sensor was synchronized with a three-dimensional motion capture system. Nineteen gait parameters, including duration, variability, and asymmetry, were calculated from initial contact and final contact event times. The statistical analysis result showed that all three groups has good to excellent agreement for four most commonly reported gait characteristic (stride-, step-, stance-,and swing-duration). Meanwhile, for asymmetry and variability, good agreement between two systems only found on subject with stroke group.
The second aim for this study is to classified the gait patterns of normal and stroke participants by using time- and frequency-domain features obtained from data provided by an Inertial Measurement Unit (IMU) sensor placed on the subject’s lower back (L5). Twenty-three participants were included and divided into two groups: healthy group (young and older adults) and stroke group. A feature selection method comprising statistical analysis and Signal-to-Noise Ratio (SNR) calculation was used to reduce the number of features. The features were used to train four Support Vector Machine (SVM) kernels, and the results were subsequently compared. The quadratic SVM kernel had the highest accuracy (93.46%), as evaluated through cross validation. Moreover, when different datasets were used on model testing, both the quadratic and cubic kernels showed the highest accuracy (96.55%). These results demonstrate the effectiveness of this study’s classification method in distinguishing between normal and stroke gait patterns.

Altilio, R., Paoloni, M., & Panella, M. (2017). Selection of clinical features for pattern recognition applied to gait analysis. Medical & Biological Engineering & Computing, 55(4), 685-695. doi:10.1007/s11517-016-1546-1
Aodhán, H., Eleanor, G., Lisa, A., Silvia Del, D., Alan, G., Lynn, R., & Brook, G. (2016). Validity of a wearable accelerometer to quantify gait in spinocerebellar ataxia type 6. Physiological measurement, 37(11), N105.
Arora, S., Venkataraman, V., Donohue, S., Biglan, K. M., Dorsey, E. R., & Little, M. A. (2014, 4-9 May 2014). High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones. Paper presented at the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Begg, R. K., Palaniswami, M., & Owen, B. (2005). Support vector machines for automated gait classification. IEEE Transactions on Biomedical Engineering, 52(5), 828-838.
Bergamini, E., Vannozzi, G., Bricca, A., Iosa, M., Paolucci, S., & Cappozzo, A. (2015). Gait stability indices and their relationship with clinical scale scores in stroke patients.
Brunelli, S., Paolucci, S., & Traballesi, M. (2014). Assessment of gait stability, harmony, and symmetry in subjects with lower-limb amputation evaluated by trunk accelerations. Journal of rehabilitation research and development, 51(4), 623.
Caby, B., Kieffer, S., de Saint Hubert, M., Cremer, G., & Macq, B. (2011). Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry. BioMedical Engineering OnLine, 10(1), 1. doi:10.1186/1475-925x-10-1
Das, K. D., Saji, A. J., & Kumar, C. S. (2017, 20-21 April 2017). Frequency analysis of gait signals for detection of neurodegenerative diseases. Paper presented at the 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT).
Del Din, S., Godfrey, A., Galna, B., Lord, S., & Rochester, L. (2016). Free-living gait characteristics in ageing and Parkinson’s disease: impact of environment and ambulatory bout length. Journal of neuroengineering and rehabilitation, 13(1), 46.
Del Din, S., Hickey, A., Hurwitz, N., Mathers, J. C., Rochester, L., & Godfrey, A. (2016). Measuring gait with an accelerometer-based wearable: influence of device location, testing protocol and age. Physiological measurement, 37(10), 1785.
Del Din, S., Hickey, A., Ladha, C., Stuart, S., Bourke, A. K., Esser, P., . . . Godfrey, A. (2016). Instrumented gait assessment with a single wearable: an introductory tutorial. F1000Research, 5.
Din, S. D., Godfrey, A., & Rochester, L. (2016). Validation of an Accelerometer to Quantify a Comprehensive Battery of Gait Characteristics in Healthy Older Adults and Parkinson's Disease: Toward Clinical and at Home Use. IEEE Journal of Biomedical and Health Informatics, 20(3), 838-847. doi:10.1109/JBHI.2015.2419317
Doi, T., Hirata, S., Ono, R., Tsutsumimoto, K., Misu, S., & Ando, H. (2013). The harmonic ratio of trunk acceleration predicts falling among older people: results of a 1-year prospective study. Journal of neuroengineering and rehabilitation, 10(1), 7.
Esser, P., Dawes, H., Collett, J., Feltham, M. G., & Howells, K. (2011). Assessment of spatio-temporal gait parameters using inertial measurement units in neurological populations. Gait & posture, 34(4), 558-560. doi:http://doi.org/10.1016/j.gaitpost.2011.06.018
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods, 39(2), 175-191.
Fusco, A., Morone, G., Pratesi, L., Coiro, P., Domenico De Angelis, M., & Paolucci, S. (2012). Assessment of upper-body dynamic stability during walking in patients with subacute stroke. Journal of rehabilitation research and development, 49(3), 439.
Godfrey, A., Conway, R., Meagher, D., & ÓLaighin, G. (2008). Direct measurement of human movement by accelerometry. Medical engineering & physics, 30(10), 1364-1386.
Godfrey, A., Culhane, K., & Lyons, G. (2007). Comparison of the performance of the activPAL™ Professional physical activity logger to a discrete accelerometer-based activity monitor. Medical engineering & physics, 29(8), 930-934.
Godfrey, A., Del Din, S., Barry, G., Mathers, J., & Rochester, L. (2015). Instrumenting gait with an accelerometer: a system and algorithm examination. Medical engineering & physics, 37(4), 400-407.
Godfrey, A., Del Din, S., Barry, G., Mathers, J. C., & Rochester, L. (2014). Within trial validation and reliability of a single tri-axial accelerometer for gait assessment. Paper presented at the Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE.
Goh, L., Song, Q., & Kasabov, N. (2004). A novel feature selection method to improve classification of gene expression data. Paper presented at the Proceedings of the second conference on Asia-Pacific bioinformatics-Volume 29.
Gonzalez, R. C., Alvarez, D., Lopez, A. M., & Alvarez, J. C. (2007). Modified pendulum model for mean step length estimation. Paper presented at the Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE.
Hartmann, A., Luzi, S., Murer, K., de Bie, R. A., & de Bruin, E. D. (2009). Concurrent validity of a trunk tri-axial accelerometer system for gait analysis in older adults. Gait & posture, 29(3), 444-448. doi:http://dx.doi.org/10.1016/j.gaitpost.2008.11.003
Hubble, R. P., Naughton, G. A., Silburn, P. A., & Cole, M. H. (2015). Wearable Sensor Use for Assessing Standing Balance and Walking Stability in People with Parkinson’s Disease: A Systematic Review. PloS one, 10(4), e0123705. doi:10.1371/journal.pone.0123705
Iosa, M., Marro, T., Paolucci, S., & Morelli, D. (2012). Stability and harmony of gait in children with cerebral palsy. Research in Developmental Disabilities, 33(1), 129-135. doi:https://doi.org/10.1016/j.ridd.2011.08.031
Iosa, M., Morone, G., Fusco, A., Pratesi, L., Bragoni, M., Coiro, P., . . . Paolucci, S. (2011). Effects of walking endurance reduction on gait stability in patients with stroke. Stroke research and treatment, 2012.
Iosa, M., Picerno, P., Paolucci, S., & Morone, G. (2016). Wearable inertial sensors for human movement analysis. Expert Review of Medical Devices, 13(7), 641-659. doi:10.1080/17434440.2016.1198694
Kavanagh, J. J., Morrison, S., James, D. A., & Barrett, R. (2006). Reliability of segmental accelerations measured using a new wireless gait analysis system. Journal of biomechanics, 39(15), 2863-2872.
Kirtley, C. (2006). Clinical gait analysis: theory and practice: Elsevier Health Sciences.
Kobsar, D., Olson, C., Paranjape, R., Hadjistavropoulos, T., & Barden, J. M. (2014). Evaluation of age-related differences in the stride-to-stride fluctuations, regularity and symmetry of gait using a waist-mounted tri-axial accelerometer. Gait & posture, 39(1), 553-557. doi:http://dx.doi.org/10.1016/j.gaitpost.2013.09.008
Latt, M. D., Menz, H. B., Fung, V. S., & Lord, S. R. (2009). Acceleration patterns of the head and pelvis during gait in older people with Parkinson's disease: a comparison of fallers and nonfallers. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, glp009.
Lau, H.-y., Tong, K.-y., & Zhu, H. (2009). Support vector machine for classification of walking conditions of persons after stroke with dropped foot. Human movement science, 28(4), 504-514. doi:https://doi.org/10.1016/j.humov.2008.12.003
Laudanski, A., Brouwer, B., & Li, Q. (2015). Activity classification in persons with stroke based on frequency features. Medical engineering & physics, 37(2), 180-186. doi:https://doi.org/10.1016/j.medengphy.2014.11.008
Lord, S., Galna, B., Verghese, J., Coleman, S., Burn, D., & Rochester, L. (2013). Independent Domains of Gait in Older Adults and Associated Motor and Nonmotor Attributes: Validation of a Factor Analysis Approach. The Journals of Gerontology: Series A, 68(7), 820-827. doi:10.1093/gerona/gls255
Ly, Q. T., Handojoseno, A. M. A., Gilat, M., Chai, R., Martens, K. A. E., Georgiades, M., . . . Nguyen, H. T. (2017, 11-15 July 2017). Detection of gait initiation Failure in Parkinson's disease based on wavelet transform and Support Vector Machine. Paper presented at the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
MacDougall, H. G., & Moore, S. T. (2005). Marching to the beat of the same drummer: the spontaneous tempo of human locomotion. Journal of applied physiology, 99(3), 1164-1173.
Mannini, A., Trojaniello, D., Cereatti, A., & Sabatini, A. M. (2016). A machine learning framework for gait classification using inertial sensors: application to elderly, post-stroke and huntington’s disease patients. Sensors, 16(1), 134.
McCamley, J., Donati, M., Grimpampi, E., & Mazzà, C. (2012). An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data. Gait & posture, 36(2), 316-318. doi:http://dx.doi.org/10.1016/j.gaitpost.2012.02.019
Menz, H. B., Lord, S. R., & Fitzpatrick, R. C. (2003). Acceleration patterns of the head and pelvis when walking are associated with risk of falling in community-dwelling older people. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 58(5), M446-M452.
Menz, H. B., Lord, S. R., & Fitzpatrick, R. C. (2003). Acceleration patterns of the head and pelvis when walking on level and irregular surfaces. Gait & posture, 18(1), 35-46. doi:https://doi.org/10.1016/S0966-6362(02)00159-5
Murray, M. P. (1967). GAIT AS A TOTAL PATTERN OF MOVEMENT: INCLUDING A BIBLIOGRAPHY ON GAIT. American Journal of physical medicine & rehabilitation, 46(1), 290-333.
Papavasileiou, I., Zhang, W., Wang, X., Bi, J., Zhang, L., & Han, S. (2017, 17-19 July 2017). Classification of Neurological Gait Disorders Using Multi-task Feature Learning. Paper presented at the 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).
Parisi, F., Ferrari, G., Baricich, A., Innocenzo, M. D., Cisari, C., & Mauro, A. (2016, 14-17 June 2016). Accurate gait analysis in post-stroke patients using a single inertial measurement unit. Paper presented at the 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN).
Preece, S. J., Goulermas, J. Y., Kenney, L. P., Howard, D., Meijer, K., & Crompton, R. (2009). Activity identification using body-mounted sensors—a review of classification techniques. Physiological measurement, 30(4), R1.
Salatino, G., Bergamini, E., Marro, T., Gentili, P., Iosa, M., Morelli, D., & Vannozzi, G. (2016). Gait stability assessment in Down and Prader-Willi syndrome children using inertial sensors. Gait & posture, 49, S16.
Sejdic, E., Lowry, K. A., Bellanca, J., Redfern, M. S., & Brach, J. S. (2014). A comprehensive assessment of gait accelerometry signals in time, frequency and time-frequency domains. IEEE Transactions on neural systems and rehabilitation engineering, 22(3), 603-612.
Simon, S. R. (2004). Quantification of human motion: gait analysis—benefits and limitations to its application to clinical problems. Journal of biomechanics, 37(12), 1869-1880. doi:https://doi.org/10.1016/j.jbiomech.2004.02.047
Sinclair, J., Hobbs, S. J., Protheroe, L., Edmundson, C. J., & Greenhalgh, A. (2013). Determination of gait events using an externally mounted shank accelerometer. Journal of Applied Biomechanics, 29(1), 118-122.
Sugiarto, T., Lin, Y.-J., Chang, C.-C., & Hsu, W.-C. (2017). Gait Analysis Based on an Inertial Measurement Unit Sensor: Validation of Spatiotemporal Parameters Calculation in Healthy Young and Older Adults. 2017 IEEE/SICE SII International Symposium on System Integration.
Tahafchi, P., Molina, R., Roper, J. A., Sowalsky, K., Hass, C. J., Gunduz, A., . . . Judy, J. W. (2017, 11-15 July 2017). Freezing-of-Gait detection using temporal, spatial, and physiological features with a support-vector-machine classifier. Paper presented at the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
Taraldsen, K., Chastin, S. F. M., Riphagen, I. I., Vereijken, B., & Helbostad, J. L. (2012). Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: A systematic literature review of current knowledge and applications. Maturitas, 71(1), 13-19. doi:http://dx.doi.org/10.1016/j.maturitas.2011.11.003
Weiss, A., Herman, T., Giladi, N., & Hausdorff, J. M. (2014). Objective assessment of fall risk in Parkinson's disease using a body-fixed sensor worn for 3 days. PloS one, 9(5), e96675.
Weiss, A., Herman, T., Giladi, N., & Hausdorff, J. M. (2015). New evidence for gait abnormalities among Parkinson’s disease patients who suffer from freezing of gait: insights using a body-fixed sensor worn for 3 days. Journal of Neural Transmission, 122(3), 403-410.
Whelan, N., Healy, R., Kenny, I., & Harrison, A. J. (2016). A COMPARISON OF FOOT STRIKE EVENTS USING THE FORCE PLATE AND PEAK IMPACT ACCELERATION MEASURES. Paper presented at the ISBS-Conference Proceedings Archive.
Whittle, M. W. (2014). Gait analysis: an introduction: Butterworth-Heinemann.
Yang, C.-C., & Hsu, Y.-L. (2010). A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring. Sensors, 10(8), 7772.
Zeni, J. A., Richards, J. G., & Higginson, J. S. (2008). Two simple methods for determining gait events during treadmill and overground walking using kinematic data. Gait & posture, 27(4), 710-714. doi:https://doi.org/10.1016/j.gaitpost.2007.07.007
Zhang, J., Lockhart, T. E., & Soangra, R. (2014). Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors. Annals of biomedical engineering, 42(3), 600-612.
Zheng, H., Yang, M., Wang, H., & McClean, S. (2009). Machine learning and statistical approaches to support the discrimination of neuro-degenerative diseases based on gait analysis Intelligent patient management (pp. 57-70): Springer.
Zijlstra, A., & Zijlstra, W. (2013). Trunk-acceleration based assessment of gait parameters in older persons: A comparison of reliability and validity of four inverted pendulum based estimations. Gait & posture, 38(4), 940-944. doi:http://doi.org/10.1016/j.gaitpost.2013.04.021
Zijlstra, W., & Hof, A. L. (2003). Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait & posture, 18(2), 1-10. doi:http://dx.doi.org/10.1016/S0966-6362(02)00190-X