Inertial Sensor-Based Instrumented Cane for Real-Time Walking Cane Kinematics Estimation

Falls are among the main causes of injuries in elderly individuals. Balance and mobility impairment are major indicators of fall risk in this group. The objective of this research was to develop a fall risk feedback system that operates in real time using an inertial sensor-based instrumented cane. Based on inertial sensor data, the proposed system estimates the kinematics (contact phase and orientation) of the cane. First, the contact phase of the cane was estimated by a convolutional neural network. Next, various algorithms for the cane orientation estimation were compared and validated using an optical motion capture system. The proposed cane contact phase prediction model achieved higher accuracy than the previous models. In the cane orientation estimation, the Madgwick filter yielded the best results overall. Finally, the proposed system was able to estimate both the contact phase and orientation in real time in a single-board computer.

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An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

Sensors ◽

10.3390/s21082869 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2869

Author(s):

Jiaen Wu ◽

Kiran Kuruvithadam ◽

Alessandro Schaer ◽

Richie Stoneham ◽

George Chatzipirpiridis ◽

...

Keyword(s):

Real Time ◽

Inertial Sensor ◽

Neurological Diseases ◽

Principal Component ◽

Sampling Frequency ◽

Powerful Analytical Tool ◽

Optical Motion ◽

Analysis System ◽

Optical Motion Capture ◽

Gait Monitoring

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

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A Novel Approach for Fall Risk Prediction Using the Inertial Sensor Data From the Timed-Up-and-Go Test in a Community Setting

IEEE Sensors Journal ◽

10.1109/jsen.2020.2987623 ◽

2020 ◽

Vol 20 (16) ◽

pp. 9339-9350 ◽

Cited By ~ 1

Author(s):

Yu-Cheng Hsu ◽

Yang Zhao ◽

Kuang-Hui Huang ◽

Ya-Ting Wu ◽

Javier Cabrera ◽

...

Keyword(s):

Risk Prediction ◽

Fall Risk ◽

Inertial Sensor ◽

Community Setting ◽

Sensor Data ◽

Timed Up And Go ◽

Novel Approach

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SENSOR-BASED ASSESSMENT OF FALLS RISK OF THE TIMED UP AND GO IN REAL-WORLD SETTINGS

Innovation in Aging ◽

10.1093/geroni/igz038.033 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S10-S10

Author(s):

Charlene C Quinn ◽

Barry R Greene ◽

Killian McManus ◽

Stephen J Redmond ◽

Brian Caulfield

Keyword(s):

Fall Risk ◽

Population Based ◽

Falls Prevention ◽

Sensor Data ◽

Gait Velocity ◽

Community Settings ◽

Mobility Impairment ◽

Timed Up And Go ◽

Falls Risk ◽

Slow Walking Speed

Abstract Falls are the leading cause of older adult injury and cost $50bn annually. New digital technologies can quantitatively measure falls risk. Objective is to report on a validated wearable sensor-based Timed Up and Go (QTUG) assessment detailing 11 measures of falls risk, frailty and mobility impairment in older adults in six countries in 38 clinical and community settings. Second objective is to generate individual targeted falls prevention programs. 14,611 QTUG records from 8,521 participants (63% female) (72.7±10.7 years) available for analysis. QTUG time was 13.9±7.4 s; gait velocity was 101.9±32.5 cm/s. 25.8% of patients reported falling in previous 12 months; 26.2% of patients were at high fall risk. 21.5% not reporting a fall, were high fall risk. Participants had slow walking speed (29.8%); high gait variability (19.8%); problems with transfers (17.5%). Easily captured and interpreted sensor data is useful in a population-based approach to quantify falls risk stratification.

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Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors

npj Digital Medicine ◽

10.1038/s41746-019-0204-z ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 8

Author(s):

Barry R. Greene ◽

Killian McManus ◽

Stephen J. Redmond ◽

Brian Caulfield ◽

Charlene C. Quinn

Keyword(s):

Risk Assessment ◽

Fall Risk ◽

Falls Prevention ◽

Sensor Data ◽

Emergency Department Utilization ◽

Mobility Impairment ◽

Timed Up And Go ◽

Falls Risk ◽

Assessment Protocol ◽

Risk Of Falls

AbstractFalls are among the most frequent and costly population health issues, costing $50bn each year in the US. In current clinical practice, falls (and associated fall risk) are often self-reported after the “first fall”, delaying primary prevention of falls and development of targeted fall prevention interventions. Current methods for assessing falls risk can be subjective, inaccurate, have low inter-rater reliability, and do not address factors contributing to falls (poor balance, gait speed, transfers, turning). 8521 participants (72.7 ± 12.0 years, 5392 female) from six countries were assessed using a digital falls risk assessment protocol. Data consisted of wearable sensor data captured during the Timed Up and Go (TUG) test along with self-reported questionnaire data on falls risk factors, applied to previously trained and validated classifier models. We found that 25.8% of patients reported a fall in the previous 12 months, of the 74.6% of participants that had not reported a fall, 21.5% were found to have a high predicted risk of falls. Overall 26.2% of patients were predicted to be at high risk of falls. 29.8% of participants were found to have slow walking speed, while 19.8% had high gait variability and 17.5% had problems with transfers. We report an observational study of results obtained from a novel digital fall risk assessment protocol. This protocol is intended to support the early identification of older adults at risk of falls and inform the creation of appropriate personalized interventions to prevent falls. A population-based approach to management of falls using objective measures of falls risk and mobility impairment, may help reduce unnecessary outpatient and emergency department utilization by improving risk prediction and stratification, driving more patients towards clinical and community-based falls prevention activities.

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Evaluation of Inertial Sensor Data by a Comparison with Optical Motion Capture Data of Guitar Strumming Gestures

Sensors ◽

10.3390/s20195722 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5722

Author(s):

Sérgio Freire ◽

Geise Santos ◽

Augusto Armondes ◽

Eduardo A. L. Meneses ◽

Marcelo M. Wanderley

Keyword(s):

Motion Capture ◽

Inertial Sensor ◽

Low Cost ◽

Sensor Data ◽

Level Control ◽

Interactive Music ◽

Acceleration Data ◽

Optical Motion ◽

Optical Motion Capture ◽

High Level

Computing technologies have opened up a myriad of possibilities for expanding the sonic capabilities of acoustic musical instruments. Musicians nowadays employ a variety of rather inexpensive, wireless sensor-based systems to obtain refined control of interactive musical performances in actual musical situations like live music concerts. It is essential though to clearly understand the capabilities and limitations of such acquisition systems and their potential influence on high-level control of musical processes. In this study, we evaluate one such system composed of an inertial sensor (MetaMotionR) and a hexaphonic nylon guitar for capturing strumming gestures. To characterize this system, we compared it with a high-end commercial motion capture system (Qualisys) typically used in the controlled environments of research laboratories, in two complementary tasks: comparisons of rotational and translational data. For the rotations, we were able to compare our results with those that are found in the literature, obtaining RMSE below 10° for 88% of the curves. The translations were compared in two ways: by double derivation of positional data from the mocap and by double integration of IMU acceleration data. For the task of estimating displacements from acceleration data, we developed a compensative-integration method to deal with the oscillatory character of the strumming, whose approximative results are very dependent on the type of gestures and segmentation; a value of 0.77 was obtained for the average of the normalized covariance coefficients of the displacement magnitudes. Although not in the ideal range, these results point to a clearly acceptable trade-off between the flexibility, portability and low cost of the proposed system when compared to the limited use and cost of the high-end motion capture standard in interactive music setups.

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A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors

Sensors ◽

10.3390/s21227517 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7517

Author(s):

Vânia Guimarães ◽

Inês Sousa ◽

Miguel Velhote Correia

Keyword(s):

Gait Analysis ◽

Inertial Sensors ◽

Inertial Sensor ◽

Sensor Data ◽

Coordinate Frame ◽

Complete Evaluation ◽

Gait Parameters ◽

Deep Recurrent Neural Network ◽

Optical Motion ◽

Frame Transformation

Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times. However, to deal with cumulative integration errors, additional error handling strategies are required. In this study, we propose an alternative approach based on a deep recurrent neural network to estimate heel and toe trajectories. We propose a coordinate frame transformation for stride trajectories that eliminates the dependency from previous strides and external inputs. Predicted trajectories are used to estimate an extensive set of spatiotemporal gait parameters. We evaluate the results in a dataset comprising foot-worn inertial sensor data acquired from a group of young adults, using an optical motion capture system as a reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. A good agreement is also achieved between the reference and estimated gait parameters, in particular when turning strides are excluded from the analysis. The performance of the method is shown to be robust to imperfect sensor-foot alignment conditions.

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An Intelligent In-Shoe System for Real-Time Gait Monitoring and Analysis

10.20944/preprints202103.0616.v1 ◽

2021 ◽

Author(s):

Jiaen Wu ◽

Kiran Kuruvithadam ◽

Alessandro Schaer ◽

Richie Stoneham ◽

George Chatzipirpiridis ◽

...

Keyword(s):

Real Time ◽

Inertial Sensor ◽

Neurological Diseases ◽

Principal Component ◽

Sampling Frequency ◽

Eigen Analysis ◽

Gait Characteristic ◽

Optical Motion ◽

Analysis System ◽

Gait Monitoring

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on the technologies for gait characteristic assessment, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigen-analysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

Download Full-text