scholarly journals An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5272
Author(s):  
Nicole Zahradka ◽  
Khushboo Verma ◽  
Ahad Behboodi ◽  
Barry Bodt ◽  
Henry Wright ◽  
...  
Keyword(s):  
Event Detection ◽  
Gold Standard ◽  
Kinetic Data ◽  
Force Plate ◽  
Detection Methods ◽  
Initial Contact ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Sensor Signals ◽  
Video Sensor

Video- and sensor-based gait analysis systems are rapidly emerging for use in ‘real world’ scenarios outside of typical instrumented motion analysis laboratories. Unlike laboratory systems, such systems do not use kinetic data from force plates, rather, gait events such as initial contact (IC) and terminal contact (TC) are estimated from video and sensor signals. There are, however, detection errors inherent in kinematic gait event detection methods (GEDM) and comparative study between classic laboratory and video/sensor-based systems is warranted. For this study, three kinematic methods: coordinate based treadmill algorithm (CBTA), shank angular velocity (SK), and foot velocity algorithm (FVA) were compared to ‘gold standard’ force plate methods (GS) for determining IC and TC in adults (n = 6), typically developing children (n = 5) and children with cerebral palsy (n = 6). The root mean square error (RMSE) values for CBTA, SK, and FVA were 27.22, 47.33, and 78.41 ms, respectively. On average, GED was detected earlier in CBTA and SK (CBTA: −9.54 ± 0.66 ms, SK: −33.41 ± 0.86 ms) and delayed in FVA (21.00 ± 1.96 ms). The statistical model demonstrated insensitivity to variations in group, side, and individuals. Out of three kinematic GEDMs, SK GEDM can best be used for sensor-based gait event detection.

scholarly journals Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1483 ◽  
Author(s):  
Lauren Benson ◽  
Christian Clermont ◽  
Ricky Watari ◽  
Tessa Exley ◽  
Reed Ferber
Keyword(s):  
Real World ◽  
Event Detection ◽  
Wearable Sensors ◽  
Force Plate ◽  
Detection Methods ◽  
Initial Contact ◽  
Detection Algorithms ◽  
Foot Strike ◽  
The Difference ◽  
Time Evaluation

The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions.

Toe walking in children with cerebral palsy: a possible functional role for the plantar flexors

2020 ◽  
Vol 124 (4) ◽  
pp. 1257-1269
Author(s):  
C. Beyaert ◽  
J. Pierret ◽  
R. Vasa ◽  
J. Paysant ◽  
S. Caudron
Keyword(s):  
Cerebral Palsy ◽  
Clinical Examination ◽  
Functional Role ◽  
Ankle Dorsiflexion ◽  
Initial Contact ◽  
Typically Developing ◽  
Plantar Flexors ◽  
Typically Developing Children ◽  
Children With Cerebral Palsy ◽  
Toe Walking

Adaptation to walking in negative-heel shoes was similar in typically developing children and children with cerebral palsy: it featured ankle dorsiflexion upon initial contact, even though (in the latter group) the soleus was always spastic in a clinical examination. Hence, in children with cerebral palsy, the early deceleration of ankle dorsiflexion by the plantar flexors (promoted by early flattening of the foot, and regardless of the type of footwear) may have a functional role.

Age Norms for Auditory-Perceptual Neurophonetic Parameters: A Prerequisite for the Assessment of Childhood Dysarthria

2020 ◽  
Vol 63 (4) ◽  
pp. 1071-1082
Author(s):  
Theresa Schölderle ◽  
Elisabet Haas ◽  
Wolfram Ziegler
Keyword(s):  
Assessment Tool ◽  
Developmental Trajectories ◽  
Voice Quality ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Age Norms ◽  
Elementary School Age ◽  
Speech Characteristics ◽  
Substantial Progress ◽  
Computer Based

Purpose The aim of this study was to collect auditory-perceptual data on established symptom categories of dysarthria from typically developing children between 3 and 9 years of age, for the purpose of creating age norms for dysarthria assessment. Method One hundred forty-four typically developing children (3;0–9;11 [years;months], 72 girls and 72 boys) participated. We used a computer-based game specifically designed for this study to elicit sentence repetitions and spontaneous speech samples. Speech recordings were analyzed using the auditory-perceptual criteria of the Bogenhausen Dysarthria Scales, a standardized German assessment tool for dysarthria in adults. The Bogenhausen Dysarthria Scales (scales and features) cover clinically relevant dimensions of speech and allow for an evaluation of well-established symptom categories of dysarthria. Results The typically developing children exhibited a number of speech characteristics overlapping with established symptom categories of dysarthria (e.g., breathy voice, frequent inspirations, reduced articulatory precision, decreased articulation rate). Substantial progress was observed between 3 and 9 years of age, but with different developmental trajectories across different dimensions. In several areas (e.g., respiration, voice quality), 9-year-olds still presented with salient developmental speech characteristics, while in other dimensions (e.g., prosodic modulation), features typically associated with dysarthria occurred only exceptionally, even in the 3-year-olds. Conclusions The acquisition of speech motor functions is a prolonged process not yet completed with 9 years. Various developmental influences (e.g., anatomic–physiological changes) shape children's speech specifically. Our findings are a first step toward establishing auditory-perceptual norms for dysarthria in children of kindergarten and elementary school age. Supplemental Material https://doi.org/10.23641/asha.12133380

Clash of signs: Putting bilingualism back into language evolution

2020 ◽  
Author(s):  
Steven Samuel
Keyword(s):  
Language Evolution ◽  
Modern Human ◽  
Alarm Calls ◽  
Bilingual Children ◽  
Human Language ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Modern Humans ◽  
The Real ◽  
Multiple Languages

Research and thinking into the cognitive aspects of language evolution has usually attempted to account for how the capacity for learning even one modern human language developed. Bilingualism has perhaps been thought of as something to think about only once the ‘real’ puzzle of monolingualism is solved, but this would assume in turn (and without evidence) that bilingualism evolved after monolingualism. All typically-developing children (and adults) are capable of learning multiple languages, and the majority of modern humans are at least bilingual. In this paper I ask whether by skipping bilingualism out of language evolution we have missed a trick. I propose that exposure to synonymous signs, such as food and alarm calls, are a necessary precondition for the abstracting away of sound from referent. In support of this possibility is evidence that modern day bilingual children are better at breaking this ‘word magic’ spell. More generally, language evolution should be viewed through the lens of bilingualism, as this is the end state we are attempting to explain.

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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