Wearable inertial sensors to measure gait and posture characteristic differences in older adult fallers and non-fallers: A scoping review

Patel, Mubarak; Pavić, Aleksandar; Goodwin, Victoria A

doi:10.1016/j.gaitpost.2019.10.039

Cited by 79 publications

(61 citation statements)

References 73 publications

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“…From those references, the most similar to our work is Reference [ 15 ], but in this paper, the authors use Machine Learning techniques to analyze the data, and they do not make a clinical analysis. Other interesting references to mention are References [ 21 , 22 , 23 ], which are systematic revisions of gait analysis using different wearable devices, like accelerometers but mainly inertial units. In Reference [ 22 ], the –authors analyze publications from January 1st, 2005 until December 31st, 2017.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [ 22 ], the –authors analyze publications from January 1st, 2005 until December 31st, 2017. In Reference [ 21 ], the authors analyze papers proposing the use of inertial sensors to predict falling risk. In Reference [ 23 ], the authors focus on the use of raw accelerometers data with Machine Learning algorithms, to determine if those techniques are suitable for human activity identification.…”

Section: Introductionmentioning

confidence: 99%

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Wristbands Containing Accelerometers for Objective Arm Swing Analysis in Patients with Parkinson’s Disease

Rincón

Valderrama

González

et al. 2020

Sensors

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In patients with Parkinson’s disease (PD), arm swing changes are common, even in the early stages, and these changes are usually evaluated subjectively by an expert. In this article, hypothesize that arm swing changes can be detected using a low-cost, cloud-based, wearable, sensor system that incorporates triaxial accelerometers. The aim of this work is to develop a low-cost, assistive diagnostic tool for use in quantifying the arm swing kinematics of patients with PD. Ten patients with PD and 11 age-matched, healthy subjects are included in the study. Four feature extraction techniques were applied: (i) Asymmetry estimation based on root mean square (RMS) differences between arm movements; (ii) posterior–anterior phase and cycle regularity through autocorrelation; (iii) tremor energy, established using Fourier transform analysis; and (iv) signal complexity through the fractal dimension by wavelet analysis. The PD group showed significant (p < 0.05) reductions in arm swing RMS values, higher arm swing asymmetry, higher anterior–posterior phase regularities, greater “high energy frequency” signals, and higher complexity in their XZ plane signals. Therefore, the novel, portable system provides a reliable means to support clinical practice in PD assessment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wristbands Containing Accelerometers for Objective Arm Swing Analysis in Patients with Parkinson’s Disease

Rincón

Valderrama

González

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The results showed that the patients with PCCA had difficulties walking, and less synchronization and stride length, resulting in an alteration in their gait and its phases. In another study, Patel, Pavic, & Goodwi (2020) conducted a review of the literature, the objective of which was to identify the measurements and variables of the stride and posture of senior citizens, with the aid of inertial sensors. In this study, the authors analysed articles from various databases, resulting in a total of 35 documents which deal with measurements obtained with sensors and that said measurements are correlated with traditional clinical balance tests.…”

Section: Related Workmentioning

confidence: 99%

Balance analysis in adolescents based on a motion capture platform

Callejas-Cuervo¹,

Jaime-Gil²,

Monroy-Guerrero³

2020

Journal of Human Sport and Exercise - 2020 - Spring Conferences of Sports Science

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An assessment of the level of balance in adolescents was done, using motion capture technologies (IMOCAP). The sample was 7 peoples, to whom a pre-test and then a post-test and intervention program were applied. The duration of the intervention program was 11 weeks, with a weekly basic gymnastics' session of 120 minutes. A diagnosis of anthropometric factors was carried out on characteristics such as size, with an average of 1.62 cm. (SD = 0.059), weight, with an average of 54.12 kg. (SD = 6.89) and age, with an average of 15 years with SD = 1.11. Consecutively, a pre-test for static balance was applied, using IMOCAP. An intervention program was designed, with the aim of improving corporal stability. Afterwards, the post-test was applied using IMOCAP. There was an improvement in the execution of basic gymnastic movements and greater stability was perceived. This was confirmed by the results registered by the instructor and the measurements generated by IMOCAP, which indicated a lesser degree of inclination and rotation of the torso in the post-test (+/-7.35° and 3.7° on average) for the three body axes. Most of the basic gymnastic movements help to improve body stability, corroborated by a high-precision, reliable platform with quantifiable result-delivery.

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“…With wireless and wearable technology, these sensors are small, light and inexpensive, without the grueling setup times of traditional motion capture systems. Besides, it has the advantages of long battery life and freedom from space limitations [35]- [36]. Each wearable inertial measurement unit adopts STM32F103C8T6 as the main controller and meets the processing speed requirements of data collection in this study.…”

Section: ) Instrumentsmentioning

confidence: 99%

The Influence of Treadmill on Postural Control

et al. 2020

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When people walk on a treadmill, significant differences exist in postural control if compared to walking on the ground. In this paper, a method combining linear and nonlinear analysis is proposed to evaluate the walking postural control ability. In this method, the movements of the upper body and the feet are analyzed separately, because these parts have more capacity to reflect the characteristics of people's gait. On this basis, the linear and nonlinear indicators of each part are analyzed, and the postural control ability is evaluated. The method is validated by three healthy subjects walking on both the treadmill and the ground. In treadmill walking case, the results show a smaller upper trunk acceleration (UTa) in the anterior-posterior (AP) direction. In the foot movement analysis, there are significant differences in the performance indexes related to the center of pressure (CoP) when people are walking on the treadmill and the ground. When walking on the treadmill, the velocity, acceleration, and jerk of the CoP in the AP direction are smaller than those in the ground walking case. Besides, when people walk on treadmills at uniform speeds, their feet accelerate is less in the vertical direction, and they have shorter stride length (SL) and longer stride time (ST). These results suggest that people have better postural control when walking on a treadmill. This method can be used as a means of assessing people's behavior when walking on various situations by evaluating their control posture ability.

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Wearable inertial sensors to measure gait and posture characteristic differences in older adult fallers and non-fallers: A scoping review

Cited by 79 publications

References 73 publications

Wristbands Containing Accelerometers for Objective Arm Swing Analysis in Patients with Parkinson’s Disease

Wristbands Containing Accelerometers for Objective Arm Swing Analysis in Patients with Parkinson’s Disease

Balance analysis in adolescents based on a motion capture platform

The Influence of Treadmill on Postural Control

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