Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit

Hwang, Tong-Hun; Reh, Julia; Effenberg, Alfred O.; Blume, Holger

doi:10.1109/tce.2018.2843289

Cited by 32 publications

(23 citation statements)

References 38 publications

(58 reference statements)

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“…a doctor) about the behavior of the patient. A gait classification of these two basic human activities is the foundation towards the prevention of injuries and monitoring of health conditions [3], [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…Analysis of gait is carried out with multiple synchronized cameras and optical markers placed at reference points of the body [9]. Therefore, gait features from video sequences can be extracted [7], [8], [10], [11]. This system achieves high accuracy, but it is complex and expensive and needs specialized laboratories [3], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

“…As an alternative, it has become popular to perform gait detection with wearable sensors (WS) using tri-axial accelerometers [1], [10], [14], [15]. WS can give accurate and reliable results about people's activities and behavior [2], [3], [7], [8], [16], [17]. The wearables are unobtrusive devices, which have a comfortable portability.…”

Section: Introductionmentioning

confidence: 99%

“…Atallah et al in [10] noted that, for example in walking, the direction of the head's movement is more representative of the body's movement than the trunk. For this reason, the detected accelerations in the head are notably attenuated and more regular, providing better results and a more realistic human gait analysis [4], [8], [10], [27].…”

Section: Introductionmentioning

confidence: 99%

“…Accelerometers calculate the sum of inertial linear and gravitational accelerations along its sensitive axis [7], [28]. In the last years, single-, dual-and triaxis accelerometers have allowed a recognition with high accuracy and low cost of daily life activities [5], [8], [14], [28]- [31].…”

Section: Introductionmentioning

confidence: 99%

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In-Ear Accelerometer-Based Sensor for Gait Classification

et al. 2020

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For several years, the detection of gait has been popularly implemented using wearable sensors, especially in the sports and medical areas. They are unobtrusive devices which allow to monitor individuals without the need of any ambulatory technology. Despite the fact, the optimal location of the sensor remains uncertain and dependent on the type of measurement. Ear-worn sensors provide a tactical position, robust against movement, that might be significant for gait classification. The purpose of this paper is to demonstrate the accuracy and reliability of in-ear accelerometer sensor to perform gait classification, between the activities walking and running. The data was collected from fourteen participants using an in-ear sensor called 'Cosinussº One', which contains a three-dimensional accelerometer sensor. The main characteristics between these two activities were detected using 17 time domain features, as for instance the maximums and standard deviations of the 3-axes, and 3 different window sizes were evaluated: 3.75s, 2s and 1s. Support vector machine (SVM) and k-nearest neighbors (KNN) classifiers were implemented and later compared. The total number of features was reduced to 6 for SVM and 12 for KNN preserving the same results. An accuracy over 99% for both classifiers was achieved, outperforming most of the previous studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In-Ear Accelerometer-Based Sensor for Gait Classification

et al. 2020

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Auditory Modulation of Multisensory Representations

Effenberg

Hwang

Ghai

et al. 2018

Music Technology With Swing

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Motor control and motor learning as well as interpersonal coordination are based on motor perception and emergent perceptuomotor representations. At least in early stages motor learning and interpersonal coordination are emerging heavily on visual information in terms of observing others and transforming the information into internal representations to guide owns behavior. With progressing learning, also other perceptual modalities are added when a new motor pattern is established by repeated physical exercises. In contrast to the vast majority of publications on motor learning and interpersonal coordination referring to a certain perceptual modality here we look at the perceptual system as a unitary system coordinating and unifying the information of all involved perceptual modalities. The relation between perceptual streams of different modalities, the intermodal processing and multisensory integration of information as a basis for motor control and learning will be the main focus of this contribution. Multi-/intermodal processing of perceptual streams results in multimodal representations and opens up new approaches to support motor learning and interpersonal coordination: Creating an additional perceptual stream adequately auditory movement information can be generated suitable to be integrated with information of other modalities and thereby modulating the resulting perceptuomotor representations without the need of attention and higher cognition. Here, the concept of a movement defined real-time acoustics is used to serve the auditory system in terms of an additional movement-auditory stream. Before the computational approach of kinematic real-time sonification is finally described, a special focus is directed to the level of adaptation modules of the internal models. Furthermore, this concept is compared with different approaches of additional acoustic movement information. Moreover, a perspective of this approach is given in a broad spectrum of new applications of supporting motor control and learning in sports and motor rehabilitation as well as a broad spectrum of joint action and interpersonal coordination between humans but also concerning human-robotinteraction.

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Biomechanical Approach to Stability of Intersomatic Implants in Cervical Spine

Freudiger¹

2022

Cervical Spine

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Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit

Cited by 32 publications

References 38 publications

In-Ear Accelerometer-Based Sensor for Gait Classification

In-Ear Accelerometer-Based Sensor for Gait Classification

Auditory Modulation of Multisensory Representations

Biomechanical Approach to Stability of Intersomatic Implants in Cervical Spine

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