Activity classification in users of ankle foot orthoses

Archer, Cameron M.; Lach, John; Chen, Shanshan; Abel, Mark F.; Bennett, Bradford C.

doi:10.1016/j.gaitpost.2013.06.005

Cited by 8 publications

(10 citation statements)

References 20 publications

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“…In terms of gait event detection, a gyroscope placed at the shank has proven to be acceptably accurate in healthy [15,23,24] and pathological gait when walking on level ground [24–26] and in healthy gait walking up and down an incline [27]. …”

Section: Introductionmentioning

confidence: 99%

“…Also, they can be mounted so that they remain under the clothes, improving system cosmesis, and potentially so that no specialist footwear or footwear modifications are needed. Recently, systems including inertial sensors have been proposed for the measurement of spatio-temporal gait parameters [ 10 , 11 ], joint angle [ 12 – 14 ] and for activity classification [ 15 ]. Investigations into the most appropriate sensor arrangement continues as the number and type of sensors, and their positioning and measurement algorithms, are evaluated for different applications.…”

Section: Introductionmentioning

confidence: 99%

“…Gyroscopes placed on the shank have been proposed for ambulatory gait analysis [ 12 , 13 , 16 – 19 ]; for the timing [ 20 , 21 ] and as feedback [ 14 ] of FES systems; and for a control system for lower limb prostheses and orthoses [ 22 ]. In terms of gait event detection, a gyroscope placed at the shank has proven to be acceptably accurate in healthy [ 15 , 23 , 24 ] and pathological gait when walking on level ground [ 24 – 26 ] and in healthy gait walking up and down an incline [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gait Event Detection during Stair Walking Using a Rate Gyroscope

Formento

Acevedo

Ghoussayni

et al. 2014

Sensors

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Abstract:Gyroscopes have been proposed as sensors for ambulatory gait analysis and functional electrical stimulation systems. These applications often require detection of the initial contact (IC) of the foot with the floor and/or final contact or foot off (FO) from the floor during outdoor walking. Previous investigations have reported the use of a single gyroscope placed on the shank for detection of IC and FO on level ground and incline walking. This paper describes the evaluation of a gyroscope placed on the shank for determination of IC and FO in subjects ascending and descending a set of stairs. Performance was compared with a reference pressure measurement system. The absolute mean difference between the gyroscope and the reference was less than 45 ms for IC and better than 135 ms for FO for both activities. Detection success was over 93%. These results provide preliminary evidence supporting the use of a gyroscope for gait event detection when walking up and down stairs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gait Event Detection during Stair Walking Using a Rate Gyroscope

Formento

Acevedo

Ghoussayni

et al. 2014

Sensors

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“…In the first class of techniques, the signal is divided into consecutive windows of fixed length, which, in the case of physical activities (level walking and stair walking) lie in the range of 1–10 s [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. One limitation of this approach is that if an activity lasts for shorter or longer time periods than the pre-defined chosen window length, the subsequent classification might be affected.…”

Section: Introductionmentioning

confidence: 99%

“…When dealing with a physical activity recognition problem, it has been argued that the de-noising of the inertial sensor data is necessary in order to be able both to extract the relevant information [ 6 , 21 , 22 , 23 ] and to identify the gait events from smooth signals [ 9 , 13 , 15 , 17 , 24 , 25 ]. De-noising can be considered as the combination of the pre-processing steps that are done to minimize the effect of noise (e.g., by filtering) and sensor misplacements (e.g., by considering a reference position for the sensor).…”

Section: Introductionmentioning

confidence: 99%

Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors

Fida

Bernabucci

Bibbo

et al. 2015

Sensors

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Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. For monitoring and fitness applications, it is crucial to develop methods able to segment each activity cycle, e.g., a gait cycle, so that the successive classification step may be more accurate. To increase detection accuracy, pre-processing is often used, with a concurrent increase in computational cost. In this paper, the effect of pre-processing operations on the detection and classification of locomotion activities was investigated, to check whether the presence of pre-processing significantly contributes to an increase in accuracy. The pre-processing stages evaluated in this study were inclination correction and de-noising. Level walking, step ascending, descending and running were monitored by using a shank-mounted inertial sensor. Raw and filtered segments, obtained from a modified version of a rule-based gait detection algorithm optimized for sequential processing, were processed to extract time and frequency-based features for physical activity classification through a support vector machine classifier. The proposed method accurately detected >99% gait cycles from raw data and produced >98% accuracy on these segmented gait cycles. Pre-processing did not substantially increase classification accuracy, thus highlighting the possibility of reducing the amount of pre-processing for real-time applications.

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