Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.
Purpose: Third trimester maternal perception of fetal movements is often used to assess fetal well-being. However, its true clinical value is unknown, primarily because of the variability in subjective quantification. The actograph, a technology available on most cardiotocograph machines, quantifies movements, but has never previously been investigated in relation to fetal health and existing monitoring devices. The objective of this study was to quantify actograph output in healthy third trimester pregnancies and investigate this in relation to other methods of assessing fetal well-being.
Methods: Forty-two women between 24 and 34 weeks of gestation underwent ultrasound scan followed by a computerized cardiotocograph (CTG). Post capture analysis of the actograph recording was performed and expressed as a percentage of activity over time. The actograph output results were analyzed in relation to Doppler, ultrasound and CTG findings expressed as z-score normalized for gestation.
Results: There was a significant association between actograph output recording and estimated fetal weight Z-score (R¼0.546, p .005). This activity was not related to estimated fetal weight. Increased actograph activity was negatively correlated with umbilical artery pulsatility index Z-score (R ¼ 0.306, p ¼ .049) and middle cerebral artery pulsatility index Z-score.
Conclusion: Fetal movements assessed by the actograph are associated both with fetal size in relation to gestation and fetoplacental Doppler parameters. It is not the case that larger babies move more, however, as the relationship with actograph output related only to estimated fetal weight z-score. These findings suggest a plausible link between the frequency of fetal movements and established markers of fetal health.
Rationale: The objective of this study was to quantify actograph output in healthy third trimester pregnancies and investigate this in relation to other methods of assessing fetal well-being. This is a widely available method of assessing fetal movements objectively, which has been shown to be an important marker of fetal health. This research is novel in the fact that actograph has never been truly investigated in relation to fetal well-being, despite being available on most cardiotocograph (CTG) machines. Our results show that fetal movements assessed by the actograph are associated both with fetal size in relation to gestation and fetoplacental Doppler parameters. If this proves to be true, smaller babies that move less maybe at particular perinatal risk.
Electromyography (EMG) is the standard technology for monitoring muscle activity in laboratory environments, either using surface electrodes or fine wire electrodes inserted into the muscle. Due to limitations such as cost, complexity, and technical factors, including skin impedance with surface EMG and the invasive nature of fine wire electrodes, EMG is impractical for use outside of a laboratory environment. Mechanomyography (MMG) is an alternative to EMG, which shows promise in pervasive applications. The present study used an exerting squat-based task to induce muscle fatigue. MMG and EMG amplitude and frequency were compared before, during, and after the squatting task. Combining MMG with inertial measurement unit (IMU) data enabled segmentation of muscle activity at specific points: entering, holding, and exiting the squat. Results show MMG measures of muscle activity were similar to EMG in timing, duration, and magnitude during the fatigue task. The size, cost, unobtrusive nature, and usability of the MMG/IMU technology used, paired with the similar results compared to EMG, suggest that such a system could be suitable in uncontrolled natural environments such as within the home.
We introduce a novel magnetic angular rate gravity (MARG) sensor fusion algorithm for inertial measurement. The new algorithm improves the popular gradient descent (ʻMadgwick’) algorithm increasing accuracy and robustness while preserving computational efficiency. Analytic and experimental results demonstrate faster convergence formultiple variations of the algorithm through changing magnetic inclination. Furthermore, decoupling of magnetic field variance from roll and pitch estimation is proven for enhanced robustness. The algorithm is validated in a human-machine interface (HMI) case study. The case study involves hardware implementation for wearable robot teleoperation in both Virtual Reality (VR) and in real-time on a 14 degree-of-freedom(DoF) humanoid robot. The experiment fuses inertial (movement) and mechanomyography(MMG) muscle sensing to control robot arm movement and grasp simultaneously, demonstrating algorithm efficacy and capacity to interface with other physiological sensors. To our knowledge, this is the first such formulation and the first fusion of inertial measurement and MMG in HMI. We believe the new algorithm holds the potential to impact a verywide range of inertial measurement applications where full orientation necessary. Physiological sensor synthesis and hardware interface further provides a foundation forrobotic teleoperation systems with necessary robustness for use in the field.
Networked operation of unmanned air vehicles (UAVs) demands fusion of information from disparate sources for accurate flight control. In this investigation, a novel sensor fusion architecture for detecting aircraft runway and horizons as well as enhancing the awareness of surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented in real-world situations due to signal misalignment. We address this through a registration step to align EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented, and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. The fusion architecture developed in this study holds promise for incorporation into manned heads-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provides a basis for rule selection in other signal fusion applications.
Fetal movements (FM) are a key factor in clinical management of high-risk pregnancies such as fetal growth restriction. While maternal perception of reduced FM can trigger self-referral to obstetric services, maternal sensation is highly subjective. Objective, reliable monitoring of fetal movement patterns outside clinical environs is not currently possible. A wearable and non-transmitting system capable of sensing fetal movements over extended periods of time would be extremely valuable, not only for monitoring individual fetal health, but also for establishing normal levels of movement in the population at large. Wearable monitors based on accelerometers have previously been proposed as a means of tracking FM, but such systems have difficulty separating maternal and fetal activity and have not matured to the level of clinical use. We introduce a new wearable system based on a novel combination of accelerometers and bespoke acoustic sensors as well as an advanced signal processing architecture to identify and discriminate between types of fetal movements. We validate the system with concurrent ultrasound tests on a cohort of 44 pregnant women and demonstrate that the garment is capable of both detecting and discriminating the vigorous, whole-body ‘startle’ movements of a fetus. These results demonstrate the promise of multimodal sensing for the development of a low-cost, non-transmitting wearable monitor for fetal movements.
We present a new bioinspired bicondylar knee joint that requires a smaller actuator size when compared to a constant moment arm joint. Unlike existing prosthetic joints, the proposed mechanism replicates the elastic, rolling and sliding elements of the human knee. As a result, the moment arm that the actuators can impart on the joint changes as function of the angle, producing the equivalent of a variable transmission. By employing a similar moment arm—angle profile as the human knee the peak actuator force for stair ascent can be reduced by 12% compared to a constant moment arm joint addressing critical impediments in weight and power for robotics limbs. Additionally, the knee employs mechanical 'ligaments' containing stretch sensors to replicate the neurosensory and compliant elements of the joint. We demonstrate experimentally how the ligament stretch can be used to estimate joint angle, therefore overcoming the difficulty of sensing position in a bicondylar joint.
In this paper we present a design methodology for a bicondylar joint that mimics many of the physical mechanisms in the human knee. We replicate the elastic ligaments and sliding and rolling joint surfaces. As a result the centre of rotation and moment arm from the quadriceps changes as a function of flexion angle in a similar way to the human knee. This leads to a larger moment arm in the centre of motion, where it is most needed for high load tasks, and a smaller moment arm at the extremes, reducing the required actuator displacement. This is anticipated to improve performance:weight ratio in legged devices for tasks such as stair accent and sit-to-stand. In the design process ligament attachment positions, femur profile and ligament lengths were taken from cadaver studies. This information was then used as inputs to a simplified kinematic computer model in order to design a valid profile for a tibial condyle. A physical model was then tested on a custom built squatting robot. It was found that although ligament lengths deviated from the designed values the robot moment arm still matched the model to within 6.1% on average. This shows that the simplified model is an effective design tool for this type of joint. It is anticipated that this design, when employed in walking robots, prostheses or exoskeletons, will improve the high load task capability of these devices. In this paper we have outlined and validated a design method to begin to achieve this goal.
This paper introduces a method which uses feedforward neural networks (FNNs) for estimating gait cycle progress using data recorded from inertial and muscle activity sensors attached to one side of the lower body. Three-axis inertial measurement unit (IMU) readings from accelerometers and gyroscopes located above the outer ankle and knee were fused with mechanomyogram (MMG) sensor readings from across major muscle groups on the left leg. Validation was against ground truth gathered concurrently with VICON motion capture. The performance was characterised by rms error (Erms) and max error (Emax), averaged across four cross-validated trials, and enhanced by adjusting number of sliding window frames and hidden layer neurons. The final configuration estimated gait cycle progress with Erms of 1.6% and Emax of 6.8%. This demonstrates promise for such a method to be used for control of unilateral robotic prostheses and exoskeletons, providing state estimation of gait progress from low power sensors limited to one side of the lower body.
Muscle activity and human motion are useful parameters to map the diagnosis, treatment, and rehabilitation of neurological and movement disorders. In laboratory and clinical environments, electromyography (EMG) and motion capture systems enable the collection of accurate, high resolution data on human movement and corresponding muscle activity. However, controlled surroundings limit both the length of time and the breadth of activities that can be measured. Features of movement, critical to understanding patient progress, can change during the course of a day and daily activities may not correlate to the limited motions examined in a laboratory. We introduce a system to measure motion and muscle activity simultaneously over the course of a day in an uncontrolled environment with minimal preparation time and ease of implementation that enables daily usage. Our system combines a bespoke inertial measurement unit (IMU) and mechanomyography (MMG) sensor, which measures the mechanical signal of muscular activity. The IMU can collect data continuously, and transmit wirelessly, for up to 10 hours. We describe the hardware design and validation and outline the data analysis (including data processing and activity classification algorithms) for the sensing system. Furthermore, we present two pilot studies to demonstrate utility of the system, including activity identification in six able-bodied subjects with an accuracy of 98%, and monitoring motion/muscle changes in a subject with cerebral palsy and of a single leg amputee over extended periods (~5 hours). We believe these results provide a foundation for mapping human muscle activity and corresponding motion changes over time, providing a basis for a range of novel rehabilitation therapies.