Hierarchical information fusion for global displacement estimation in microsensor motion capture

Decision fusion in healthcare and medicine: a narrative review

OBJECTIVE

To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels.

BACKGROUND

The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information.

METHODS

We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review.

CONCLUSIONS

Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.

Validity and Sensitivity of an Inertial Measurement Unit-Driven Biomechanical Model of Motor Variability for Gait

Motor variability in gait is frequently linked to fall risk, yet field-based biomechanical joint evaluations are scarce. We evaluated the validity and sensitivity of an inertial measurement unit (IMU)-driven biomechanical model of joint angle variability for gait. Fourteen healthy young adults completed seven-minute trials of treadmill gait at several speeds and arm swing amplitudes. Trunk, pelvis, and lower-limb joint kinematics were estimated by IMU- and optoelectronic-based models using OpenSim. We calculated range of motion (ROM), magnitude of variability (meanSD), local dynamic stability (λmax), persistence of ROM fluctuations (DFAα), and regularity (SaEn) of each angle over 200 continuous strides, and evaluated model accuracy (RMSD: root mean square difference), consistency (ICC2,1: intraclass correlation), biases, limits of agreement, and sensitivity to within-participant gait responses (effects of speed and swing). RMSDs of joint angles were 1.7-9.2° (pooled mean of 4.8°), excluding ankle inversion. ICCs were mostly good to excellent in the primary plane of motion for ROM and in all planes for meanSD and λmax, but were poor to moderate for DFAα and SaEn. Modelled speed and swing responses for ROM, meanSD, and λmax were similar. Results suggest that the IMU-driven model is valid and sensitive for field-based assessments of joint angle time series, ROM in the primary plane of motion, magnitude of variability, and local dynamic stability.

Estimating Lower Limb Kinematics Using a Reduced Wearable Sensor Count

GOAL

This paper presents an algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors.

METHODS

The algorithm makes novel use of a constrained Kalman filter (CKF). The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, zero velocity update, flat-floor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints).

RESULTS

Evaluation of the algorithm using an optical motion capture-based sensor-to-segment calibration on nine participants (7 men and 2 women, weight [Formula: see text] kg, height [Formula: see text] m, age [Formula: see text] years old), with no known gait or lower body biomechanical abnormalities, who walked within a [Formula: see text] m ² capture area shows that it can track motion relative to the mid-pelvis origin with mean position and orientation (no bias) root-mean-square error (RMSE) of [Formula: see text] cm and [Formula: see text], respectively. The sagittal knee and hip joint angle RMSEs (no bias) were [Formula: see text] and [Formula: see text], respectively, while the corresponding correlation coefficient (CC) values were [Formula: see text] and [Formula: see text].

CONCLUSION

The CKF-based algorithm was able to track the 3D pose of the pelvis, thigh, and shanks using only three inertial sensors worn on the pelvis and shanks.

SIGNIFICANCE

Due to the Kalman-filter-based algorithm's low computation cost and the relative convenience of using only three wearable sensors, gait parameters can be computed in real-time and remotely for long-term gait monitoring. Furthermore, the system can be used to inform real-time gait assistive devices.

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).

Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor

Walking speed is widely used to study human health status. Wearable inertial measurement units (IMU) are promising tools for the ambulatory measurement of walking speed. Among wearable inertial sensors, the ones worn on the wrist, such as a watch or band, have relatively higher potential to be easily incorporated into daily lifestyle. Using the arm swing motion in walking, this paper proposes a regression model-based method for longitudinal walking speed estimation using a wrist-worn IMU. A novel kinematic variable is proposed, which finds the wrist acceleration in the principal axis (i.e. the direction of the arm swing). This variable (called pca-acc) is obtained by applying sensor fusion on IMU data to find the orientation followed by the use of principal component analysis. An experimental evaluation was performed on 15 healthy young subjects during free walking trials. The experimental results show that the use of the proposed pca-acc variable can significantly improve the walking speed estimation accuracy when compared to the use of raw acceleration information (p<0.01). When Gaussian process regression is used, the resulting walking speed estimation accuracy and precision is about 5.9% and 4.7%, respectively.

A magnetometer-free indoor human localization based on loosely coupled IMU/UWB fusion

The magnetic distortions in indoor environment affects the accuracy of yaw angle estimation using magnetometer. Thus, the accuracy of indoor localization based on inertial-magnetic sensors will be affected as well. To address this issue, this paper proposes a magnetometer-free solution for indoor human localization and yaw angle estimation. The proposed algorithm fuses a wearable inertial sensor consisting of MEMS-based accelerometer and gyroscope with a portable ultra-wideband (UWB) localization system in a cascaded two-step filter consisting of a tilt Kalman filter and a localization Kalman filter. By benchmarking against an optical motion capture system, the experimental results show that the proposed algorithm can accurately track position and velocity as well as the yaw angle without using magnetometer.

Gait phase detection in able-bodied subjects and dementia patients

Accurate detection of gait phases allows identification of specific functional deficits at each phase of the gait cycle for motor function assessment. This paper proposes a robust gait phase detection method to identify the seven gait phases in overground walking for normal and pathologic gaits. Four inertial sensors are used to obtain knee angles, tibia angles and feet angular rate patterns in the sagittal plane. The key events segmenting the gait cycles are searched using an adaptive threshold in adaptive searching intervals to make sure it works well for different subjects with high variation in cadence and step length during walking. The subjects involved in this study are categorized into three groups: five healthy adult subjects, two healthy elderly subjects and two severe dementia patients. The experimental results have shown our method can reliably detect all gait phases for able-bodied subjects and dementia patients without subject-specific calibration.

Reliability of the step phase detection using inertial measurement units: pilot study

The use of inertial sensors for the gait event detection during a long-distance walking, for example, on different surfaces and with different walking patterns, is important to evaluate the human locomotion. Previous studies demonstrated that gyroscopes on the shank or foot are more reliable than accelerometers and magnetometers for the event detection in case of normal walking. However, these studies did not link the events with the temporal parameters used in the clinical practice; furthermore, they did not clearly verify the optimal position for the sensors depending on walking patterns and surface conditions. The event detection quality of the sensors is compared with video, used as ground truth, according to the parameters proposed by the Gait and Clinical Movement Analysis Society. Additionally, the performance of the sensor on the foot is compared with the one on the shank. The comparison is performed considering both normal walking and deviations to the walking pattern, on different ground surfaces and with or without constraints on movements. The preliminary results show that the proposed methodology allows reliable detection of gait events, even in case of abnormal footfall and in slipping surface conditions, and that the optimal location to place the sensors is the shank.