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Seung-uk Ko, Gerald J. Jerome, Eleanor M. Simonsick, Stephanie Studenski and Luigi Ferrucci

, & Dieppe, 1992 ; O’Reilly, Muir, & Doherty, 1996 ) and increases with age ( Peat, McCarney, & Croft, 2001 ), was associated with slower gait speed, longer double support time, and smaller range of motion (ROM) in hip and knee joints while walking ( Bindawas, 2016 ; Kitayuguchi et al., 2015 ; Ko

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Roel De Ridder, Julien Lebleu, Tine Willems, Cedric De Blaiser, Christine Detrembleur and Philip Roosen

lower for parameters based on final foot contact (stance, swing, and double support time) as larger errors in event determination of final foot contact were observed. To improve validity, future research should focus on ameliorating IMU algorithms for identifying this final foot contact. Limitations of

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Neelesh K. Nadkarni, Karl Zabjek, Betty Lee, William E. McIlroy and Sandra E. Black

Changes in gait parameters induced by the concomitant performance of one of two cognitive tasks activating working memory and spatial attention, was examined in healthy young adults (YA) and older adults (OA). There was a main effect of task condition on gait-speed (p = .02), stride-length (p < .001) and double-support time (p = .04) independent of the group. There were no significant differences between working memory and spatial attention associated gait changes. Working-memory and spatial-attention dual-tasking led to a decrease in gait-speed (p = .09 and 0.01) and stride-length (p = .04 and 0.01) and increase in double-support time (p = .01 and 0.03) in YA and decrease in stride-length (p = .04 and 0.01) alone in OA. Cognitive task associated changes in gait may be a function of limited attentional resources irrespective of the type of cognitive task.

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Ferdous Wahid, Rezaul Begg, Noel Lythgo, Chris J. Hass, Saman Halgamuge and David C. Ackland

Normalization of gait data is performed to reduce the effects of intersubject variations due to physical characteristics. This study reports a multiple regression normalization approach for spatiotemporal gait data that takes into account intersubject variations in self-selected walking speed and physical properties including age, height, body mass, and sex. Spatiotemporal gait data including stride length, cadence, stance time, double support time, and stride time were obtained from healthy subjects including 782 children, 71 adults, 29 elderly subjects, and 28 elderly Parkinson’s disease (PD) patients. Data were normalized using standard dimensionless equations, a detrending method, and a multiple regression approach. After normalization using dimensionless equations and the detrending method, weak to moderate correlations between walking speed, physical properties, and spatiotemporal gait features were observed (0.01 < |r| < 0.88), whereas normalization using the multiple regression method reduced these correlations to weak values (|r| < 0.29). Data normalization using dimensionless equations and detrending resulted in significant differences in stride length and double support time of PD patients; however the multiple regression approach revealed significant differences in these features as well as in cadence, stance time, and stride time. The proposed multiple regression normalization may be useful in machine learning, gait classification, and clinical evaluation of pathological gait patterns.

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Edgar R. Vieira, Ruth Tappen, Sareen S. Gropper, Maria T. Severi, Gabriella Engstrom, Marcio R. de Oliveira, Alexandre C. Barbosa and Rubens A. da Silva

center of 1 foot during 2 subsequent steps and the center of the opposite foot during mid stance. • Swing and stance time - time from toe off to heel strike and time from heel strike to toe off. • Single and double support time - time that one or both feet are on the floor simultaneously. These gait

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Brian D. Street and William Gage

spatiotemporal gait measures were calculated: gait speed, step length, step width, and single and double support time. Gait speed was determined by calculating the time required for the sternum marker to travel the distance between the first and last heel contact within this capture volume space. Step length was

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Karim Korchi, Frédéric Noé, Noëlle Bru and Thierry Paillard

wearing their own shoes (_SH) or being barefoot (_BF). The following spatiotemporal gait variables were calculated: veloctiy (VEL), cadence (CAD), stride length (STRL), step length (SL), contact time (CT) of the foot with the ground from initial heel contact to toe-off, double support time (DST), and

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Jinkyu Lee, Yong-Jin Yoon and Choongsoo S. Shin

It is common for soldiers to carry a heavy backpack and a rifle over unpredictable terrain during military training and/or operations. The effects of load carriage on human locomotion have been reported, including decreased step length, increased step frequency, increased double support time, and

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Jaimie A. Roper, Ryan T. Roemmich, Mark D. Tillman, Matthew J. Terza and Chris J. Hass

plane gait variables (eg, step length and double support time) in individuals poststroke. 8 , 9 Additionally, sagittal plane kinetic profiles exhibit differences between limbs during split-belt treadmill walking, and also in ipsilateral limb comparisons during speed-matched conventional walking. 10

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Daniel Hamacher, Dennis Hamacher, Roy Müller, Lutz Schega and Astrid Zech

instance, that older cohorts in particular show significant increases in gait variability parameters when walking at slower speeds. Although the researchers reported changes in step width, step time, swing time, stance time, and double support time, they did not examine MTC variability. There is nothing in