Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults

Choi, Jungyeon; Parker, Sheridan; Knarr, Brian A.; Gwon, Yeongjin; Youn, Jong Hoon

doi:10.3390/s21206831

Cited by 19 publications

(12 citation statements)

References 48 publications

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“…For the step event detection and feature extraction methods, the same methods as in our previous works were used [ 24 , 25 ]. We used a peak detection method for the step detection by recognizing the highest peak of vertical acceleration.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Stability during Walking at Simulated Ship’s Rolling Motion Using Accelerometers

Choi

Knarr

Gwon

et al. 2022

Sensors

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Due to a ship’s extreme motion, there is a risk of injuries and accidents as people may become unbalanced and be injured or fall from the ship. Thus, individuals must adjust their movements when walking in an unstable environment to avoid falling or losing balance. A person’s ability to control their center of mass (COM) during lateral motion is critical to maintaining balance when walking. Dynamic balancing is also crucial to maintain stability while walking. The margin of stability (MOS) is used to define this dynamic balancing. This study aimed to develop a model for predicting balance control and stability in walking on ships by estimating the peak COM excursion and MOS variability using accelerometers. We recruited 30 healthy individuals for this study. During the experiment, participants walked for two minutes at self-selected speeds, and we used a computer-assisted rehabilitation environment (CAREN) system to simulate the roll motion. The proposed prediction models in this study successfully predicted the peak COM excursion and MOS variability. This study may be used to protect and save seafarers or passengers by assessing the risk of balance loss.

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Section: Methodsmentioning

confidence: 99%

“…A total 60 features were normalized by centering data and then used for this study. Detailed methods for the step detection and feature extraction are well described in [ 24 , 25 ], respectively.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Stability during Walking at Simulated Ship’s Rolling Motion Using Accelerometers

Choi

Knarr

Gwon

et al. 2022

Sensors

Self Cite

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show abstract

“…We extracted features that have demonstrated to be useful in mobility studies [ 36 , 37 ]. First, 20 base features were extracted, of which the descriptions and formulas are presented in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

“…This was repeated 12 times using the leave-one-subject-out as the test set protocol. Gait studies on mobility have demonstrated that good prediction metrics can be achieved with only eight to 10 features while balancing model complexity and computational cost [ 35 , 37 ]. Hence, we selected only the top 10 features at each iteration.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor

Sossa

Nam

et al. 2022

Sensors

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The walkability of a neighborhood impacts public health and leads to economic and environmental benefits. The condition of sidewalks is a significant indicator of a walkable neighborhood as it supports and encourages pedestrian travel and physical activity. However, common sidewalk assessment practices are subjective, inefficient, and ineffective. Current alternate methods for objective and automated assessment of sidewalk surfaces do not consider pedestrians’ physiological responses. We developed a novel classification framework for the detection of irregular walking surfaces that uses a machine learning approach to analyze gait parameters extracted from a single wearable accelerometer. We also identified the most suitable location for sensor placement. Experiments were conducted on 12 subjects walking on good and irregular walking surfaces with sensors attached at three different locations: right ankle, lower back, and back of the head. The most suitable location for sensor placement was at the ankle. Among the five classifiers trained with gait features from the ankle sensor, Support Vector Machine (SVM) was found to be the most effective model since it was the most robust to subject differences. The model’s performance was improved with post-processing. This demonstrates that the SVM model trained with accelerometer-based gait features can be used as an objective tool for the assessment of sidewalk walking surface conditions.

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“…A step event was recognized by detecting heel-strike using the peak detection algorithm with data obtained by accelerometers [22], [23].…”

Section: Data Processing 1) Step Time Variabilitymentioning

confidence: 99%

The Effects of Ship’s Roll Motion on the Center of Mass and Margin of Stability During Walking: A Simulation Study

2022

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Walking strategies in an unstable environment like a ship differ from walking on stable ground. Extreme ship motions may endanger the safety of the crews. Notably, a loss of balance on board can lead to an injury or an accident of falling off a ship. Keeping one's balance on board a ship is strongly influenced by the ship's motion. Therefore, the objective of this study is to determine how walking on a ship differs from walking in a stable environment and explore the effects of the ship's roll motion on balance control and stability while walking in sea environments. We hypothesized that step time variability, center of mass (COM), and margin of stability (MOS) would significantly differ between stable and unstable walking conditions. We also hypothesized that there would be an effect of rolling cycles and angles on increasing step time variability, COM excursion, and MOS variability. We recruited 30 healthy individuals between 21 and 39 years old for this study. Participants walked for two minutes at their self-selected speeds during the study with and without rolling on a computer-assisted rehabilitation environment (CAREN) system. The CAREN system was used to simulate the parametric roll motion of ships up to 20 degrees. This study quantified step time variability, peak COM excursion, and MOS variability in different rolling conditions. We found a significant difference in step time variability (p < 0.001), lateral peak COM excursion (p < 0.001), and MOS variability (p < 0.001) between waking on land and walking at sea. INDEX TERMS CAREN, center of mass, lateral balance, margin of stability, ship's roll motion, walking

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Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults

Cited by 19 publications

References 48 publications

Prediction of Stability during Walking at Simulated Ship’s Rolling Motion Using Accelerometers

Prediction of Stability during Walking at Simulated Ship’s Rolling Motion Using Accelerometers

Machine Learning Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor

The Effects of Ship’s Roll Motion on the Center of Mass and Margin of Stability During Walking: A Simulation Study

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