Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Leiva, Karla Miriam Reyes; Jaén-Vargas, Milagros; Codina, Benito; Olmedo, José Javier Serrano

doi:10.3390/s21144767

Cited by 14 publications

(7 citation statements)

References 132 publications

(245 reference statements)

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“…As the fall is detected the scope increments including other needs to be treated using human activity recognition (HAR). Those oriented to sports ( Zhuang & Xue, 2019 ) and rehabilitation ( Panwar et al, 2019 ; Xing et al, 2020 ); as well as degenerative diseases that involve loss of mobility such as Parkinson’s disease and knee osteoarthritis ( Slade et al, 2021 ; Tan et al, 2021 ), and in assisted living which presents solutions for elderly people and also for people with visual impairments ( Reyes Leiva et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models

Jaén-Vargas

Leiva

Fernandes

et al. 2022

PeerJ Computer Science

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Deep learning (DL) models are very useful for human activity recognition (HAR); these methods present better accuracy for HAR when compared to traditional, among other advantages. DL learns from unlabeled data and extracts features from raw data, as for the case of time-series acceleration. Sliding windows is a feature extraction technique. When used for preprocessing time-series data, it provides an improvement in accuracy, latency, and cost of processing. The time and cost of preprocessing can be beneficial especially if the window size is small, but how small can this window be to keep good accuracy? The objective of this research was to analyze the performance of four DL models: a simple deep neural network (DNN); a convolutional neural network (CNN); a long short-term memory network (LSTM); and a hybrid model (CNN-LSTM), when variating the sliding window size using fixed overlapped windows to identify an optimal window size for HAR. We compare the effects in two acceleration sources’: wearable inertial measurement unit sensors (IMU) and motion caption systems (MOCAP). Moreover, short sliding windows of sizes 5, 10, 15, 20, and 25 frames to long ones of sizes 50, 75, 100, and 200 frames were compared. The models were fed using raw acceleration data acquired in experimental conditions for three activities: walking, sit-to-stand, and squatting. Results show that the most optimal window is from 20–25 frames (0.20–0.25s) for both sources, providing an accuracy of 99,07% and F1-score of 87,08% in the (CNN-LSTM) using the wearable sensors data, and accuracy of 98,8% and F1-score of 82,80% using MOCAP data; similar accurate results were obtained with the LSTM model. There is almost no difference in accuracy in larger frames (100, 200). However, smaller windows present a decrease in the F1-score. In regard to inference time, data with a sliding window of 20 frames can be preprocessed around 4x (LSTM) and 2x (CNN-LSTM) times faster than data using 100 frames.

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Section: Introductionmentioning

confidence: 99%

Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models

Jaén-Vargas

Leiva

Fernandes

et al. 2022

PeerJ Computer Science

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“…Therefore, assistive technology development and validation should involve insights from visually impaired individuals. The insufficient involvement of end-users is a limitation of the current literature; development processes must consider targeted users to ensure that usability and accuracy are not compromised [ 6 ]. The dataset created in this research could also be beneficial as an input for deep learning models that train quantitative parameters of walking, as evidenced by a recent review [ 18 ]; there is a need for research into gait in free-living conditions, i.e., at end-user residences.…”

Section: Discussionmentioning

confidence: 99%

“…Smartphone-based inertial sensing, utilizing deep learning methods, requires extensive data for training and may not be tailored specifically to the unique gait patterns of VIP [ 5 ]. Furthermore, a recent review highlighted the lack of inertial sensor systems designed for VIP and the scarcity of literature on IMU-based biomechanical analysis in VIP-oriented applications [ 6 ]. While wearable inertial sensors have gained attention in clinical research for the gait parameters of people with conditions such as stroke, Parkinson’s and multiple sclerosis, there is currently a dearth of studies focusing on VIP biomechanics [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Spatio-Temporal Parameters of Gait and Posture of Visually Impaired People Using Wearable Sensors

Reyes Leiva,

Gato,

Olmedo

2023

Sensors

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In rehabilitating orientation and mobility (O&M) for visually impaired people (VIP), the measurement of spatio-temporal gait and postural parameters is of specific interest for rehabilitators to assess performance and improvements in independent mobility. In the current practice of rehabilitation worldwide, this assessment is carried out in people with estimates made visually. The objective of this research was to propose a simple architecture based on the use of wearable inertial sensors for quantitative estimation of distance traveled, step detection, gait velocity, step length and postural stability. These parameters were calculated using absolute orientation angles. Two different sensing architectures were tested for gait according to a selected biomechanical model. The validation tests included five different walking tasks. There were nine visually impaired volunteers in real-time acquisitions, where the volunteers walked indoor and outdoor distances at different gait velocities in their residences. The ground truth gait characteristics of the volunteers in five walking tasks and an assessment of the natural posture during the walking tasks are also presented in this article. One of the proposed methods was selected for presenting the lowest absolute error of the calculated parameters in all of the traveling experimentations: 45 walking tasks between 7 and 45 m representing a total of 1039 m walked and 2068 steps; the step length measurement was 4.6 ± 6.7 cm with a mean of 56 cm (11.59 Std) and 1.5 ± 1.6 relative error in step count, which compromised the distance traveled and gait velocity measurements, presenting an absolute error of 1.78 ± 1.80 m and 7.1 ± 7.2 cm/s, respectively. The results suggest that the proposed method and its architecture could be used as a tool for assistive technology designed for O&M training to assess gait parameters and/or navigation, and that a sensor placed in the dorsal area is sufficient to detect noticeable postural changes that compromise heading, inclinations and balancing in walking tasks.

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“…They are capable of imparting huge amounts of data using minimal energy. Tracking the motion of a visually impaired person or the movement of humans within a small area can easily be implemented by pedestrian dead reckoning, which is an example of dead reckoning technology ( 11 ). Newborn systems based on indoor positioning have been seen to work when using aerial robots, mobile robots ( 12 ), and humanoid robots.…”

Section: Selection Of Relevant Techniques and Technology According To...mentioning

confidence: 99%

Indoor Positioning Systems: A Blessing for Seamless Object Identification, Monitoring, and Tracking

Shyam

Juliet

Kirubakaran

2022

Front. Public Health

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Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Cited by 14 publications

References 132 publications

Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models

Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models

Estimation of Spatio-Temporal Parameters of Gait and Posture of Visually Impaired People Using Wearable Sensors

Indoor Positioning Systems: A Blessing for Seamless Object Identification, Monitoring, and Tracking

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