Smartphone-Based Assessment of Gait During Straight Walking, Turning, and Walking Speed Modulation in Laboratory and Free-Living Environments

As turns and walking speed modulation are crucial for functional mobility, development of a field-based tool to objectively evaluate non-steady-state gait is essential. This study aimed to quantify spatiotemporal gait using three Android smartphones during steady-state walking, turns, and gait speed modulation in laboratory and free-living environments. In total, 24 adults ambulated along a 10-m walkway in both environments under seven conditions: straight walking, 90° left or right turn, and modulating gait speed from usual-slow, usual-fast, slow-fast, and fast-slow. Two smartphones were attached to the body, with another phone placed in a shoulder bag. Gait velocity, step time, step length, cadence, and symmetry were computed from smartphone-based tri-axial accelerometers and validated with motion capture and video, in laboratory and free-living environments, respectively. Validity was assessed using Pearson's correlation and Bland–Altman analysis. Gait velocity results revealed moderate to very high validity across all walking conditions, smartphone models, smartphone locations, and environments. Correlations for gait velocity ranged between 0.87–0.91 and 0.79–0.83 for straight walking, 0.86–0.95 and 0.86–0.89 for turning, and 0.51–0.90 and 0.67–0.89 for speed modulation trials, in laboratory and free-living environments, respectively. Step time, step length, and cadence demonstrated high to very high correlations for straight walking and turns. However, symmetry results revealed high correlations only during straight walking in the laboratory. Conditions that included slow walking showed negligible to moderate validity with a high bias. In conclusion, smartphones can be employed as field-based devices to assess steady-state walking, turning, and speed modulation across environment, model, and placement when walking faster than 0.5 m/s.

[1]  Benjamin Long,et al.  Validity and reliability of smartphone orientation measurement to quantify dynamic balance function , 2018, Physiological measurement.

[2]  Fumiko Ichikawa,et al.  A Cross Culture Study on Phone Carrying and Physical Personalization , 2007, HCI.

[3]  Shigeo Tanabe,et al.  Validity of gait asymmetry estimation by using an accelerometer in individuals with hemiparetic stroke , 2017, Journal of physical therapy science.

[4]  S. Studenski,et al.  Gait speed and survival in older adults. , 2011, JAMA.

[5]  M. Mukaka,et al.  Statistics corner: A guide to appropriate use of correlation coefficient in medical research. , 2012, Malawi medical journal : the journal of Medical Association of Malawi.

[6]  Kara K. Patterson,et al.  Gait asymmetry in community-ambulating stroke survivors. , 2008, Archives of physical medicine and rehabilitation.

[7]  Ben Stansfield,et al.  Characteristics of very slow stepping in healthy adults and validity of the activPAL3™ activity monitor in detecting these steps. , 2015, Medical engineering & physics.

[8]  A Stefanie Mikolaizak,et al.  Gait parameter risk factors for falls under simple and dual task conditions in cognitively impaired older people. , 2013, Gait & posture.

[9]  N. A. Abu Osman,et al.  Are the spatio-temporal parameters of gait capable of distinguishing a faller from a non-faller elderly? , 2014, European journal of physical and rehabilitation medicine.

[10]  A Leardini,et al.  Estimation of spatial-temporal gait parameters in level walking based on a single accelerometer: Validation on normal subjects by standard gait analysis , 2012, Comput. Methods Programs Biomed..

[11]  Andrzej Wit,et al.  Comparison of four methods of calculating the symmetry of spatial-temporal parameters of gait. , 2014, Acta of bioengineering and biomechanics.

[12]  Babak Ziaie,et al.  A wearable smartphone-enabled camera-based system for gait assessment. , 2015, Gait & posture.

[13]  Vipul Lugade,et al.  Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body, bag, belt, hand, and pocket. , 2017, Gait & posture.

[14]  Greta C Bernatz,et al.  How humans walk: bout duration, steps per bout, and rest duration. , 2008, Journal of rehabilitation research and development.

[15]  Alan Godfrey,et al.  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 , 2016, IEEE Journal of Biomedical and Health Informatics.

[16]  A. Godfrey,et al.  Instrumenting gait with an accelerometer: A system and algorithm examination , 2015, Medical engineering & physics.

[17]  Lauren A. Grieco,et al.  Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies , 2015, JMIR mHealth and uHealth.

[18]  Philip Schatz,et al.  Validating the Accuracy of Reaction Time Assessment on Computer-Based Tablet Devices , 2015, Assessment.

[19]  F Huxham,et al.  Defining spatial parameters for non-linear walking. , 2006, Gait & posture.

[20]  Nigel H. Lovell,et al.  Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement , 2015, Sensors.

[21]  Greta C Bernatz,et al.  Video task analysis of turning during activities of daily living. , 2007, Gait & posture.

[22]  G. ÓLaighin,et al.  Direct measurement of human movement by accelerometry. , 2008, Medical engineering & physics.

[23]  Wiebren Zijlstra,et al.  Assessment of spatio-temporal parameters during unconstrained walking , 2004, European Journal of Applied Physiology.

[24]  Nigel H. Lovell,et al.  Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different , 2015, Medical & Biological Engineering & Computing.

[25]  M A Brodie,et al.  Big data vs accurate data in health research: Large-scale physical activity monitoring, smartphones, wearable devices and risk of unconscious bias. , 2018, Medical hypotheses.

[26]  J. Frank,et al.  Turning behavior in healthy older adults: Is there a preference for step versus spin turns? , 2010, Gait & posture.

[27]  Lynn Rochester,et al.  Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach. , 2013, The journals of gerontology. Series A, Biological sciences and medical sciences.