Dataset Information

On the Orientation Error of IMU: Investigating Static and Dynamic Accuracy Targeting Human Motion.

ABSTRACT: The accuracy in orientation tracking attainable by using inertial measurement units (IMU) when measuring human motion is still an open issue. This study presents a systematic quantification of the accuracy under static conditions and typical human dynamics, simulated by means of a robotic arm. Two sensor fusion algorithms, selected from the classes of the stochastic and complementary methods, are considered. The proposed protocol implements controlled and repeatable experimental conditions and validates accuracy for an extensive set of dynamic movements, that differ in frequency and amplitude of the movement. We found that dynamic performance of the tracking is only slightly dependent on the sensor fusion algorithm. Instead, it is dependent on the amplitude and frequency of the movement and a major contribution to the error derives from the orientation of the rotation axis w.r.t. the gravity vector. Absolute and relative errors upper bounds are found respectively in the range [0.7° ÷ 8.2°] and [1.0° ÷ 10.3°]. Alongside dynamic, static accuracy is thoroughly investigated, also with an emphasis on convergence behavior of the different algorithms. Reported results emphasize critical issues associated with the use of this technology and provide a baseline level of performance for the human motion related application.

SUBMITTER: Ricci L

PROVIDER: S-EPMC5017605 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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On the Orientation Error of IMU: Investigating Static and Dynamic Accuracy Targeting Human Motion.

Ricci Luca L Taffoni Fabrizio F Formica Domenico D

PloS one 20160909 9

The accuracy in orientation tracking attainable by using inertial measurement units (IMU) when measuring human motion is still an open issue. This study presents a systematic quantification of the accuracy under static conditions and typical human dynamics, simulated by means of a robotic arm. Two sensor fusion algorithms, selected from the classes of the stochastic and complementary methods, are considered. The proposed protocol implements controlled and repeatable experimental conditions and v ...[more]

PMID: 27612100

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Project description:Magneto-inertial measurement units (MIMU) are a suitable solution to assess human motor performance both indoors and outdoors. However, relevant quantities such as step width and base of support, which play an important role in gait stability, cannot be directly measured using MIMU alone. To overcome this limitation, we developed a wearable platform specifically designed for human movement analysis applications, which integrates a MIMU and an Infrared Time-of-Flight proximity sensor (IR-ToF), allowing for the estimate of inter-object distance. We proposed a thorough testing protocol for evaluating the IR-ToF sensor performances under experimental conditions resembling those encountered during gait. In particular, we tested the sensor performance for different (i) target colors; (ii) sensor-target distances (up to 200 mm) and (iii) sensor-target angles of incidence (AoI) (up to 60 ∘ ). Both static and dynamic conditions were analyzed. A pendulum, simulating the oscillation of a human leg, was used to generate highly repeatable oscillations with a maximum angular velocity of 6 rad/s. Results showed that the IR-ToF proximity sensor was not sensitive to variations of both distance and target color (except for black). Conversely, a relationship between error magnitude and AoI values was found. For AoI equal to 0 ∘ , the IR-ToF sensor performed equally well both in static and dynamic acquisitions with a distance mean absolute error <1.5 mm. Errors increased up to 3.6 mm (static) and 11.9 mm (dynamic) for AoI equal to ± 30 ∘ , and up to 7.8 mm (static) and 25.6 mm (dynamic) for AoI equal to ± 60 ∘ . In addition, the wearable platform was used during a preliminary experiment for the estimation of the inter-foot distance on a single healthy subject while walking. In conclusion, the combination of magneto-inertial unit and IR-ToF technology represents a valuable alternative solution in terms of accuracy, sampling frequency, dimension and power consumption, compared to existing technologies.

Project description:Motion boundaries (local changes in visual motion direction) arise naturally when objects move relative to an observer. In human visual cortex, neuroimaging studies have identified a region (the kinetic occipital area [KO]) that responds more strongly to motion-boundary stimuli than to transparent-motion stimuli. However, some functional magnetic resonance imaging (fMRI) studies suggest that KO may encompass multiple visual areas and single-unit studies in macaque visual cortex have identified neurons selective for motion-boundary orientation in areas V2, V3, and V4, implying that motion-boundary selectivity may not be restricted to a single area. It is not known whether fMRI responses to motion boundaries are selective for motion-boundary orientation, as would be expected if these responses reflected the population activity of motion-boundary-selective neurons. We used an event-related fMRI adaptation protocol to measure orientation-selective responses to motion boundaries in human visual cortex. On each trial, we measured the response to a probe stimulus presented after an adapter stimulus (a vertical or horizontal motion-boundary grating). The probe stimulus was either a motion-boundary grating oriented parallel or orthogonal to the adapter stimulus or a transparent-motion stimulus. Orientation-selective adaptation for motion boundaries--smaller responses for trials in which test and adapter stimuli were parallel to each other--was observed in multiple extrastriate visual areas. The strongest adaptation, relative to the unadapted responses, was found in V3A, V3B, LO1, LO2, and V7. Most of the visual areas that exhibited orientation-selective adaptation in our data also showed response preference for motion boundaries over transparent motion, indicating that most of the human visual areas previously shown to respond to motion boundaries are also selective for motion-boundary orientation. These results suggest that neurons selective for motion-boundary orientation are distributed across multiple human visual cortical areas and argue against the existence of a single region or area specialized for motion-boundary processing.

Dataset Information

On the Orientation Error of IMU: Investigating Static and Dynamic Accuracy Targeting Human Motion.

Publications

On the Orientation Error of IMU: Investigating Static and Dynamic Accuracy Targeting Human Motion.

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