Motion Signal Processing Research Articles

A novel Short-time Fourier transform-based algorithm for detection of wandering in the patients with Alzheimer’s disease

ABSTRACT As the elderly population rises, the growth rate of age-related diseases such as Alzheimer’s disease and cognitive impairments increases. Wandering is one of the first and the most progressive and challenging behaviors; that manifest with certain patterns in the mobility behavior of the patients in the early stages of the disease. Timely diagnosis of wandering can prevent irreparable damages, including the risk of losing the patient, severe physical injuries caused by accidents, and even death. So far, numerous studies have focused on the development of wandering detection algorithms. Most of these mainly rely on two approaches of extracting features from patients’ trajectory history and estimating the path complexity. Motion signal processing methods have rarely been used in this area. An important consideration for these patients is the instability in their movement behaviors, which has received limited attention in previous research, leading to less compatibility of models with this feature. Therefore, this paper proposes an algorithm based on motion signal processing using the Short-time Fourier transform (STFT). This algorithm detects wandering patterns only by using the patient’s trajectory data and changes in their zero and non-zero frequency components. The efficiency of the proposed algorithm has been evaluated using the Geolife open-source dataset, considering the macro-average of metrics such as accuracy, precision, specificity, recall, and F-score, achieving respective values of 96.38%, 94.89%, 96.36%, 96.36%, and 95.58%. The results have validated the proposed algorithm’s strong performance in diagnosing wandering behavior, which, on one hand, helps prevent adverse consequences and, on the other hand, aids in the diagnosis and predicting the progression of the disease to severe Alzheimer’s stage.

The influence of form on motion signal processing in the ventral intraparietal area of macaque monkeys

The visual system relies on both motion and form signals to perceive the direction of self-motion, yet the coordination mechanisms between these two elements in this process remain elusive. In the current study, we employed heading perception as a model to delve into the interaction characteristics between form and motion signals. We recorded the responses of neurons in the ventral intraparietal area (VIP), an area with strong heading selectivity, to motion-only, form-only, and combined stimuli of simulated self-motion. Intriguingly, VIP neurons responded to form-only cues defined by Glass patterns, although they exhibited no tuning selectivity. In combined condition, introducing a small offset between form and motion cues significantly enhanced neuronal sensitivity to motion cues. However, with a larger offset, the enhancement effect on sensitivity became comparatively smaller. Moreover, we observed that the influence of form cues on neuronal response to motion cues is more pronounced in the later stage (1–2 s) of stimulation, with a relatively smaller effect in the early stage (0–1 s). This suggests a dynamic interaction between motion and form cues over time for heading perception. In summary, our study uncovered that in area VIP, form information plays a role in constructing accurate self-motion perception. This adds valuable insights into the complex dynamics of how the brain integrates motion and form cues for the perception of one's own movements.

Application of artificial intelligence wearable devices based on neural network algorithm in mass sports activity evaluation

Based on the rapid development of big data, cloud computing, Internet of things and other technologies in recent years, intelligent hardware devices has been applied to all aspects of life. Under this background, some scholars have put forward relevant concepts such as “Smart Life”. In the field of mass sports life, through the development and application of science and technology, there has been new changes related to the application of neural network algorithm technology and intelligent hardware devices. Therefore, artificial intelligence wearable devices based on wearable technology came into being. This paper analyzes the application of this device in mass sports activities. Then, this paper describes the key research technologies of motion data processing based on neural network algorithm, including: depth frame differential convolution neural network structure, motion data extraction method, human motion signal processing algorithm, etc.; then it analyzes the action recognition and interaction system design based on Intelligent wearable devices. Finally, it analyzes the recognition results of human action system, the accuracy of human action recognition system and the factors that affect the performance of the recognition system. It is concluded that the artificial intelligent wearable devices designed in this paper can be well used in popular sports activities. Finally, it introduces the research on the evaluation strategy of popular sports activities based on artificial intelligence, and hopes that this equipment can help public sports activities. This paper studies the neural network algorithm and applies it to the design process of artificial intelligence wearable devices, which promotes the development of mass sports activity evaluation.

Motion Signal Processing for a Remote Gas Metal Arc Welding Application

This article covers the signal processing for a human–robot remote controlled welding application. For this purpose, a test and evaluation system is under development. It allows a skilled worker to weld in real time without being exposed to the associated physical stress and hazards. The torch movement of the welder in typical welding tasks is recorded by a stereoscopic sensor system. Due to a mismatch between the speed of the acquisition and the query rate for data by the robot control system, a prediction has to be developed. It should generate a suitable tool trajectory from the acquired data, which has to be a C 2 -continuous function. For this purpose, based on a frequency analysis, a Kalman-Filter in combination with a disturbance observer is applied. It reproduces the hand movement with sufficient accuracy and lag-free. The required algorithm is put under test on a real-time operating system based on Linux and Preempt_RT in connection to a KRC4 robot controller. By using this setup, the welding results in a plane are of good quality and the robot movement coincides with the manual movement sufficiently.

Real-time optical tracking for motion compensated irradiation with scanned particle beams at CNAO

PurposeWe describe the interface developed at the National Center for Oncological Hadrontherapy in Pavia to provide the dose delivery systems with real time respiratory motion information captured with an optical tracking system. An experimental study is presented to assess the technical feasibility of the implemented organ motion compensation framework, by analyzing the film response when irradiated with proton beams. MethodsThe motion monitoring solution is based on a commercial hardware for motion capture running in-house developed software for respiratory signal processing. As part of the integration, the latency of data transmission to the dose delivery system was experimentally quantified and accounted for by signal time prediction. A respiratory breathing phantom is presented and used to test tumor tracking based either on the optical measurement of the target position or internal-external correlation models and beam gating, as driven by external surrogates. Beam tracking was tested considering the full target motion excursion (25×18mm), whereas it is limited to 6×2mm in the gating window. The different motion mitigation strategies were evaluated by comparing the experimental film responses with respect to static irradiation conditions. Dose inhomogeneity (IC) and conformity (CI) are provided as main indexes for dose quality assessment considering the irradiation in static condition as reference. ResultsWe measured 20.6ms overall latency for motion signal processing. Dose measurements showed that beam tracking largely preserved dose homogeneity and conformity, showing maximal IC and CI variations limited to +0.10 and −0.01 with respect to the static reference. Gating resulted in slightly larger discrepancies (ΔIC=+0.20, ΔCI=−0.13) due to uncompensated residual motion in the gating window. ConclusionsThe preliminary beam tracking and gating results verified the functionality of the prototypal solution for organ motion compensation based on optical monitoring.

Research on Applications of Wavelet Transform Threshold Method to Strong Motion Signal Processing

Fourier transform and short-time Fourier transform are the main methods in signal analysis, which can reflect the spectrum signature of signals in the whole time domain; but they cannot be used in the multi-resolution analysis on the non-stationary signals. However, the wavelet transform overcome the limits of Fourier and short-time Fourier transform, which can be performed in accurate time-frequency analysis of signals. Furthermore, the diversity of wavelet functions makes the wavelet transform more adaptive and flexible. Applying the wavelet transform to seismic signal processing is the complement and improvement of existing processing methods. In this paper, the basic theory of the wavelet threshold denoising method and its application to the strong motion signal processing were mainly introduced. The high-frequency noises were removed, and simultaneously the high-frequency signals were effectively retained.

Research on Applications of Wavelet Transform Threshold Method to Strong Motion Signal Processing

Fourier transform and short-time Fourier transform are the main methods in signal analysis, which can reflect the spectrum signature of signals in the whole time domain; but they cannot be used in the multi-resolution analysis on the non-stationary signals. However, the wavelet transform overcome the limits of Fourier and short-time Fourier transform, which can be performed in accurate time-frequency analysis of signals. Furthermore, the diversity of wavelet functions makes the wavelet transform more adaptive and flexible. Applying the wavelet transform to seismic signal processing is the complement and improvement of existing processing methods. In this paper, the basic theory of the wavelet threshold denoising method and its application to the strong motion signal processing were mainly introduced. The high-frequency noises were removed, and simultaneously the high-frequency signals were effectively retained.

Expressive Facial Animation Synthesis by Learning Speech Coarticulation and Expression Spaces

Synthesizing expressive facial animation is a very challenging topic within the graphics community. In this paper, we present an expressive facial animation synthesis system enabled by automated learning from facial motion capture data. Accurate 3D motions of the markers on the face of a human subject are captured while he/she recites a predesigned corpus, with specific spoken and visual expressions. We present a novel motion capture mining technique that "learns" speech coarticulation models for diphones and triphones from the recorded data. A Phoneme-Independent Expression Eigenspace (PIEES) that encloses the dynamic expression signals is constructed by motion signal processing (phoneme-based time-warping and subtraction) and Principal Component Analysis (PCA) reduction. New expressive facial animations are synthesized as follows: First, the learned coarticulation models are concatenated to synthesize neutral visual speech according to novel speech input, then a texture-synthesis-based approach is used to generate a novel dynamic expression signal from the PIEES model, and finally the synthesized expression signal is blended with the synthesized neutral visual speech to create the final expressive facial animation. Our experiments demonstrate that the system can effectively synthesize realistic expressive facial animation.

Perceptual asynchrony: motion leads color

It is widely accepted that motion and color are processed in separate brain areas of primates. Numerous studies on monkeys suggest that neural mechanisms responsible for motion processing respond faster than those for color. Recent studies on humans, however, provide contradictory evidence. Is this discrepancy due to a gap between species (animal vs. human), or between measures (neurophysiological vs. behavioral)? To help resolve this issue, event-related potentials were acquired as human participants viewed motion and color stimuli. Results indicated that the physiological response evoked by motion arose earlier than that by color, which is consistent with previous findings in animals. This temporal precedence of motion signal processing over color was corroborated in a parallel behavioral experiment.

Constraint-based motion adaptation

Today's computer animators have access to many systems and techniques to author high-quality motion. Unfortunately, available techniques typically produce a particular motion for a specific character. In this paper we present a constraint-based approach to adapt previously created motions to new situations and characters. We combine constraint methods that compute changes to motion to meet specified needs with motion signal processing methods that modify signals yet preserve desired properties of the original motion. The combination allows the adaptation of motions to meet new goals while retaining much of the motion's original quality. © 1998 John Wiley & Sons, Ltd.

The creative process of animating human movement

The computer animation of articulated models such as human figures can be regarded as a complex synthesis task much like composing a musical piece or designing a new product. Developing computer animation tools requires a sound understanding of both this creative process as well as the animation task itself. Most current commercial animation packages focus on providing useful but low-level functions to create, modify and compose human movement. This paper proposes a hierarchical approach to human figure animation which incorporates knowledge on several levels about movement and motion processing in order to support the creative process and facilitate the motion specification task without taking control away from the animator. Two examples are given to illustrate this principle: a tool for animation of human locomotion, and motion signal processing techniques to adapt existing motion data.