Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning.

Notification systems that convey urgency without adding cognitive burden are crucial in human-computer interaction. Haptic feedback systems, particularly those utilizing vibration feedback, have emerged as a compelling solution, capable of providing desirable levels of urgency depending on the application. High-risk applications require an evaluation of the urgency level elicited during critical notifications. Traditional evaluations of perceived urgency rely on subjective self-reporting and performance metrics, which, while useful, are not real-time and can be distracting from the task at hand. In contrast, EEG technology offers a direct, non-intrusive method of assessing the user's cognitive state. Leveraging deep learning, this study introduces a novel approach to evaluate perceived urgency from single-trial EEG data, induced by vibration stimuli on the upper body, utilizing our newly collected urgency-via-vibration dataset. The proposed model combines a 2D convolutional neural network with a temporal convolutional network to capture spatial and temporal EEG features, outperforming several established EEG models. The proposed model achieves an average classification accuracy of 83% through leave-one-subject-out cross-validation across three urgency classes (not urgent, urgent, and very urgent) from a single trial of EEG data. Furthermore, explainability analysis showed that the prefrontal brain region, followed by the central brain region, are the most influential in predicting the urgency level. A follow-up neural statistical analysis revealed an increase in event-related synchronization (ERS) in the theta frequency band (4-7 Hz) with the increased level of urgency, which is associated with high arousal and attention in the neuroscience literature. A limitation of this study is that the proposed model's performance was tested only the urgency-via-vibration dataset, which may affect the generalizability of the findings.

EEG correlates to perceived urgency elicited by vibration stimulation of the upper body.

Conveying information effectively while minimizing user distraction is critical to human-computer interaction. As the proliferation of audio-visual communication pushes human information processing capabilities to the limit, researchers are turning their attention to haptic interfaces. Haptic feedback has the potential to create a desirable sense of urgency that allows users to selectively focus on events/tasks or process presented information with minimal distraction or annoyance. There is a growing interest in understanding the neural mechanisms associated with haptic stimulation. In this study, we aim to investigate the EEG correlates associated with the perceived urgency elicited by vibration stimuli on the upper body using a haptic vest. A total of 31 participants enrolled in this experiment and were exposed to three conditions: no vibration pattern (NVP), urgent vibration pattern (UVP), and very urgent vibration pattern (VUVP). Through self-reporting, participants confirmed that the vibration patterns elicited significantly different levels of perceived urgency (Friedman test, Holm-Bonferroni correction, p < 0.01). Furthermore, neural analysis revealed that the power spectral density of the delta, theta, and alpha frequency bands in the middle central area (C1, Cz, and C2) significantly increased for the UVP and VUVP conditions as compared to the NVP condition (One-way ANOVA test, Holm-Bonferroni correction, p < 0.01). While the perceptual experience of haptic-induced urgency is well studied with self-reporting and behavioral evidence, this is the first effort to evaluate the neural correlates to haptic-induced urgency using EEG. Further research is warranted to identify unique correlates to the cognitive processes associated with urgency from sensory feedback correlates.

Alpha interbrain synchrony during mediated interpersonal touch.

Interpersonal touch plays a crucial role in human communication, development, and wellness. Mediated interpersonal touch (MIT), a technology to distance or virtually simulated interpersonal touch, has received significant attention to counteract the negative consequences of touch deprivation. Studies investigating the effectiveness of MIT have primarily focused on self-reporting or behavioral correlates. It is largely unknown how MIT affects neural processes such as interbrain functional connectivity during human interactions. Given how users exchange haptic information simultaneously during interpersonal touch, interbrain functional connectivity provides a more ecologically valid way of studying the neural correlates associated with MIT. In this study, a palm squeeze task is designed to examine interbrain synchrony associated with MIT using EEG-based hyperscanning methodology. The phase locking value (PLV) index is used to measure interbrain synchrony. Results demonstrate that MIT elicits a significant increase in alpha interbrain synchronization between participants' brains. Especially, there was a significant difference in the alpha PLV indices between no MIT and MIT conditions in the early stage (130-470 ms) of the interaction period (t-test, p < 0.05). Given the role that alpha interbrain synchrony plays during social interaction, a significant increase in PLV index during MIT interaction seems to indicate an effect of social coordination. The findings and limitations of this study are further discussed, and perspectives on future research are provided.

Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients.

Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.

An ensemble deep-learning approach for single-trial EEG classification of vibration intensity.

Objective. Single-trial electroencephalography (EEG) classification is a promising approach to evaluate the cognitive experience associated with haptic feedback. Convolutional neural networks (CNNs), which are among the most widely used deep learning techniques, have demonstrated their effectiveness in extracting EEG features for the classification of different cognitive functions, including the perception of vibration intensity that is often experienced during human-computer interaction. This paper proposes a novel CNN ensemble model to classify the vibration-intensity from a single trial EEG data that outperforms the state-of-the-art EEG models.Approach. The proposed ensemble model, named SE NexFusion, builds upon the observed complementary learning behaviors of the EEGNex and TCNet Fusion models, exhibited in learning personal as well generic neural features associated with vibration intensity. The proposed ensemble employs multi-branch feature encoders corroborated with squeeze-and-excitation units that enables rich-feature encoding while at the same time recalibrating the weightage of the obtained feature maps based on their discriminative power. The model takes in a single trial of raw EEG as an input and does not require complex EEG signal-preprocessing.Main results. The proposed model outperforms several state-of-the-art bench-marked EEG models by achieving an average accuracy of 60.7% and 61.6% under leave-one-subject-out and within-subject cross-validation (three-classes), respectively. We further validate the robustness of the model through Shapley values explainability method, where the most influential spatio-temporal features of the model are counter-checked with the neural correlates that encode vibration intensity.Significance. Results show that SE NexFusion outperforms other benchmarked EEG models in classifying the vibration intensity. Additionally, explainability analysis confirms the robustness of the model in attending to features associated with the neural correlates of vibration intensity.

Neural correlates of thermal stimulation during active touch.

Introduction: Thermal feedback technologies have been explored in human-computer interaction to provide secondary information and enhance the overall user experience. Unlike fast-response haptic modalities such as vibration and force feedback, the human brain's processes associated with thermal feedback are not fully understood. Methods: In this study, we utilize electroencephalography (EEG) brain imaging to systematically examine the neural correlates associated with a wide range of thermal stimuli, including 9, 15, 32, and 42°C, during active touch at the fingertip. A custom experimental setup is developed to provide thermal stimulation at the desirable temperature levels. A total of 30 participants are recruited to experience the four levels of thermal stimulation by actively touching a thermal stimulation unit with the index finger while recording brain activities via EEG. Time-frequency analysis and power spectral density (PSD) of the EEG data are utilized to analyze the delta, theta, alpha, beta, and gamma frequency bands. Results: The results show that the delta, theta, and alpha PSDs of 9 and 15°C stimuli are significantly higher than the PSDs of 32 and 42°C in the right frontal area during the early stage of the stimulation, from 282 ms up to 1,108 ms (One-way ANOVA test, Holm-Bonferroni correction, p < 0.05). No significant differences in PSDs are found between 9 and 15°C thermal stimuli or between 32 and 42°C thermal stimuli. Discussion: The findings of this study inform the development of thermal feedback system in human-computer interaction.

Assessment of EEG-based functional connectivity in response to haptic delay.

Haptic technologies enable users to physically interact with remote or virtual environments by applying force, vibration, or motion via haptic interfaces. However, the delivery of timely haptic feedback remains a challenge due to the stringent computation and communication requirements associated with haptic data transfer. Haptic delay disrupts the realism of the user experience and interferes with the quality of interaction. Research efforts have been devoted to studying the neural correlates of delayed sensory stimulation to better understand and thus mitigate the impact of delay. However, little is known about the functional neural networks that process haptic delay. This paper investigates the underlying neural networks associated with processing haptic delay in passive and active haptic interactions. Nineteen participants completed a visuo-haptic task using a computer screen and a haptic device while electroencephalography (EEG) data were being recorded. A combined approach based on phase locking value (PLV) functional connectivity and graph theory was used. To assay the effects of haptic delay on functional connectivity, we evaluate a global connectivity property through the small-worldness index and a local connectivity property through the nodal strength index. Results suggest that the brain exhibits significantly different network characteristics when a haptic delay is introduced. Haptic delay caused an increased manifestation of the small-worldness index in the delta and theta bands as well as an increased nodal strength index in the middle central region. Inter-regional connectivity analysis showed that the middle central region was significantly connected to the parietal and occipital regions as a result of haptic delay. These results are expected to indicate the detection of conflicting visuo-haptic information at the middle central region and their respective resolution and integration at the parietal and occipital regions.

Midfrontal theta power encodes the value of haptic delay.

The use of haptic technologies in modern life scenarios is becoming the new normal particularly in rehabilitation, medical training, and entertainment applications. An evident challenge in haptic telepresence systems is the delay in haptic information. How humans perceive delayed visual and audio information has been extensively studied, however, the same for haptically delayed environments remains largely unknown. Here, we develop a visuo-haptic experimental setting that simulates pick and place task and involves continuous haptic feedback stimulation with four possible haptic delay levels. The setting is built using a haptic device and a computer screen. We use electroencephalography (EEG) to study the neural correlates that could be used to identify the amount of the experienced haptic delay. EEG data were collected from 34 participants. Results revealed that midfrontal theta oscillation plays a pivotal role in quantifying the amount of haptic delay while parietal alpha showed a significant modulation that encodes the presence of haptic delay. Based on the available literature, these results suggest that the amount of haptic delay is proportional to the neural activation that is associated with conflict detection and resolution as well as for multi-sensory divided attention.

A Comparison of Vibrotactile Feedback and Electrical Muscle Stimulation (EMS) for Motor Response During Active Hand Movement.

Wearable haptic technologies have garnered recent widespread attention due to increased accessibility, functionality, and affordability. These systems typically provide haptic feedback to augment the human ability to interact with their environment. This study compares two haptic feedback modalities, vibrotactile and EMS, against visual feedback to elicit a motor response during active hand movement. Forty-five participants, divided into three groups, performed a task to touch their face and received one of three possible sensory feedback cues, namely visual, vibrotactile, and electrical muscle stimulation (EMS), to interrupt their movement and avoid touching their face. Two quantitative performance measures are used in the comparison, the response time (time elapsed from stimulation to motor response) and the error rate (percentage that the user fails to avoid touching their face). Results showed that vibrotactile and EMS feedback yielded significantly faster response time than visual feedback, while no significant differences between vibrotactile and EMS were observed. Furthermore, the error rate was significantly lower for EMS compared to visual feedback, whereas no significant differences were observed between vibrotactile and visual feedback. In conclusion, it seems that EMS feedback is preferable for applications where errors are not tolerable (critical medical applications), whereas vibrotactile is superior for non-critical applications due to its low cost and higher usability (more pleasant compared to EMS).

Neural Coding of Vibration Intensity.

Vibrotactile feedback technology has become widely used in human-computer interaction due to its low cost, wearability, and expressiveness. Although neuroimaging studies have investigated neural processes associated with different types of vibrotactile feedback, encoding vibration intensity in the brain remains largely unknown. The aim of this study is to investigate neural processes associated with vibration intensity using electroencephalography. Twenty-nine healthy participants (aged 18-40 years, nine females) experienced vibrotactile feedback at the distal phalanx of the left index finger with three vibration intensity conditions: no vibration, low-intensity vibration (1.56 g), and high-intensity vibration (2.26 g). The alpha and beta band event-related desynchronization (ERD) as well as P2 and P3 event-related potential components for each of the three vibration intensity conditions are obtained. Results demonstrate that the ERD in the alpha band in the contralateral somatosensory and motor cortex areas is significantly associated with the vibration intensity. The average power spectral density (PSD) of the peak period of the ERD (400-600 ms) is significantly stronger for the high- and low-vibration intensity conditions compared to the no vibration condition. Furthermore, the average PSD of the ERD rebound (700-2,000 ms) is significantly maintained for the high-vibration intensity compared to low-intensity and no vibration conditions. Beta ERD signals the presence of vibration. These findings inform the development of quantitative measurements for vibration intensities based on neural signals.

Midfrontal theta oscillation encodes haptic delay.

Haptic technologies aim to simulate tactile or kinesthetic interactions with a physical or virtual environment in order to enhance user experience and/or performance. However, due to stringent communication and computational needs, the user experience is influenced by delayed haptic feedback. While delayed feedback is well understood in the visual and auditory modalities, little research has systematically examined the neural correlates associated with delayed haptic feedback. In this paper, we used electroencephalography (EEG) to study sensory and cognitive neural correlates caused by haptic delay during passive and active tasks performed using a haptic device and a computer screen. Results revealed that theta power oscillation was significantly higher at the midfrontal cortex under the presence of haptic delay. Sensory correlates represented by beta rebound were found to be similar in the passive task and different in the active task under the delayed and synchronous conditions. Additionally, the event related potential (ERP) P200 component is modulated under the haptic delay condition during the passive task. The P200 amplitude significantly reduced in the last 20% of trials during the passive task and in the absence of haptic delay. Results suggest that haptic delay could be associated with increased cognitive control processes including multi-sensory divided attention followed by conflict detection and resolution with an earlier detection during the active task. Additionally, haptic delay tends to generate greater perceptual attention that does not significantly decay across trials during the passive task.

Contactless Kinesthetic Feedback to Support Handwriting Using Magnetic Force.

Handwriting is a fundamental human skill that is essential for communication yet is one of the most complex skills to be mastered. Pen-based interaction with touchscreen devices are increasingly used in digital handwriting practices to simulate pen and paper experience, but are mostly based on auditory-visual feedback. Given that handwriting relies on visual and motor skills, haptic feedback is recently explored to augment audio-visual systems to further support the handwriting process. In this article, we present an assistive platform entitled KATIB (means writer in Arabic) that provides high fidelity kinesthetic feedback, in addition to audio-visual feedback, to support handwriting using magnetic forces. We propose novel contactless kinesthetic guidance methods, namely proactive and retroactive guidance, to guide the handwriting stylus along a desirable trajectory based on position control. Detaching the handwriting stylus from any mechanical device enables learners to have full control over grasping and moving at their own pace and style. The proposed platform is characterized for haptic interaction. Finally, a psychophysical experiment is conducted to validate that the kinesthetic guidance is perceivable and beneficial as a sensory feedback using a novel handwriting copy task. Contactless kinesthetic feedback seems to play a significant role in supporting digital handwriting by influencing the kinematics of the handwriting process.

Haptic Guidance to Support Handwriting for Children With Cognitive and Fine Motor Delays.

Handwriting is an essential skill for developing sensorimotor and intellectual skills in children. Handwriting constitutes a complex activity relying on cognitive, visual-motor, memory and linguistic abilities, and is therefore challenging to master, especially for children with learning difficulties such as those with cognitive, sensorimotor or memory deficits. Recently-emerged haptic guidance systems have a potential to facilitate the acquisition of handwriting skills in both adults and children. In this paper we present a longitudinal experimental study that examined the effects of haptic guidance to improve handwriting skills in children with cognitive and fine motor delays as a function of the handwriting complexity in terms of visual familiarity and haptic difficulty. A haptic-based handwriting training platform that provides haptic guidance along the trajectory of a handwriting task was utilized. 12 children with cognitive and fine motor delays defined in terms of intellectual difficulty (IQ score) and mild motor difficulty in pincer grasp control, participated in the study. Children were divided into two groups, a target group and a control group. The target group completed haptic-guided training and pencil-and-paper test whereas the control group took only the pencil-and-paper test without any training. A total of 32 handwriting tasks was used in the study where 16 tasks were used for training while the entire 32 tasks were completed for evaluation. Results demonstrated that the target group performed significantly better than the control group for handwriting tasks that are visually familiar but haptically difficult (Wilcoxon signed-rank test, p 0.01). An improvement was also seen in the performance of untrained tasks as well as trained tasks (Spearman's linear correlation coefficient, 0.667; p = 0.05). In addition to confirming that haptic guidance can significantly improve motor functions, this study revealed a significant effect of task difficulty (visual familiarity and haptic complexity) on the effectiveness of haptic guidance for handwriting skill acquisition for children with cognitive and fine motor delays.

EEG response to game-craving according to personal preference for games.

Recently, the World Health Organization included 'gaming disorder' in its latest revision of the international classification of diseases (ICD-11). Despite extensive research on internet gaming disorder (IGD), few studies have addressed game-related stimuli eliciting craving, which plays an important role in addiction. Particularly, most previous studies did not consider personal preferences in games presented to subjects as stimuli. In this study, we compared neurophysiological responses elicited for favorite game (FG) videos and non-favorite game (NFG) videos. We aimed to demonstrate neurophysiological characteristics according to the game preference in the IGD group. We measured participants' electroencephalogram (EEG) while they watched FG, NFG and neutral videos. For FG videos, the parieto-occipital theta power (TPPO) were significantly increased compared with those for NFG videos (P < 0.05, paired t-test). TPPO also differed significantly between the healthy control and IGD groups only on FG videos controlling covariate (TPPO on neutral videos) (P < 0.05, analysis of covariance [ANCOVA]). And TPPO was significantly correlated to self-reported craving score only on FG videos (r = 0.334, P < 0.05). In the present study, we demonstrate that FG videos induce higher TPPO than that induced by NFG videos in the IGD group and TPPO is a reliable EEG feature associated with craving for gaming.

Neural Activations Associated With Friction Stimulation on Touch-Screen Devices.

Tactile sensation largely influences human perception, for instance when using a mobile device or a touch screen. Active touch, which involves tactile and proprioceptive sensing under the control of movement, is the dominant tactile exploration mechanism compared to passive touch (being touched). This paper investigates the role of friction stimulation objectively and quantitatively in active touch tasks, in a real human-computer interaction on a touch-screen device. In this study, 24 participants completed an active touch task involved stroking the virtual strings of a guitar on a touch-screen device while recording the electroencephalography (EEG) signal. Statistically significant differences in beta and gamma oscillations in the middle frontal and parietal areas at the late period of the active touch task are found. Furthermore, stronger beta event-related desynchronization (ERD) and rebound in the presence of friction stimulation in the contralateral parietal area are observed. However, in the ipsilateral parietal area, there is a difference in beta oscillation only at the late period of the motor task. As for implicit emotion communication, a significant increase in emotional responses for valence, arousal, dominance, and satisfaction is observed when the friction stimulation is applied. It is argued that the friction stimulation felt by the participants' fingertip in a touch-screen device further induces cognitive processing compared to the case when no friction stimulation is applied. This study provides objective and quantitative evidence that friction stimulation is able to affect the bottom-up sensation and cognitive processing.

Investigating Haptic Guidance Methods for Teaching Children Handwriting Skills.

Haptics technologies have the potential to considerably improve the acquisition of handwriting skills by providing physical assistance to improve movement accuracy and precision. To date, very few studies have thoroughly examined the effectiveness of various haptic guidance methods to leverage the acquisition of handwriting skills. In this paper, we examine the role of several methods for haptic guidance, namely full haptic guidance, partial haptic guidance, disturbance haptic guidance, and no-haptic guidance toward improving the learning outcomes of handwriting skills acquisition for typical children. A group of 42 children from Cranleigh School Abu Dhabi across two educational stages, namely Foundation Stage 2 (FS2, 4-5 years old) and Year 2 (6-7 years old), participated in this study. Results showed that disturbance haptic guidance was the most effective for high complexity handwriting tasks (such as writing the letters "o" and "g"), partial haptic guidance was the most effective for medium complexity handwriting tasks (such as "t," "r," "s," "e," "n," "a," and "b"), and full haptic guidance was the most effective for low complexity letters (such as "i"). Another interesting finding was that FS2 participants had statistically significant improvement in handwriting speed compared to the Year 2 group, demonstrated by a significantly shorter test completion time. Furthermore, female children performed statistically better than their male counterparts in partial guidance. These results can be utilized to build more effective haptic-based handwriting tools for typical children.

Investigating the interference pattern of dual tasks using serial decomposition.

BACKGROUND: Specific investigation of dual task-interference (DTI) may help researchers to develop the optimal training exercise for enhancing the performance of daily activities. OBJECTIVES: To reveal the DTI by comparing the performances between personalized single tasks (cognitive or motor task) and dual task with serial decomposition in normal healthy adults. METHODS: After a preliminary period, healthy participants randomly (n = 46) performed three computerized experiments of cognitive (CT), motor (MT) and dual tasks (DT). In CT, participants were required to release button 1 (BT1) as rapidly as possible when the font color of a word and its meaning were congruent (Go), and in MT, they had to release BT1 and then tap button 2 (BT2) 10 times as rapidly as possible if the symbol "○" was presented (Go). The DT consisted of a combination of CT and MT elements. The reaction time (RT) of correct releases (RTCR) of BT1 in all tasks was measured, as well as the button shifting time between releasing BT1 and pressing BT2, and the finger tapping rate in MT and DT. To obtain the DTI values, we calculated the RTCR ratio in CT and MT and divided the outcome by the RTCR of DT. RESULTS: The ratio of RTCR in CT (% CT/DT, 78.6±13.0%) and MT (% MT/DT, 74.2±10.1%) were significantly lower than the ratio of RTCR in DT (% DT/DT, 100%). The button shifting time of MT was at 92.0±23.7% of baseline, and the finger tapping rate of MT was 106.1±19.1%, which was significantly higher than baseline. CONCLUSIONS: The % DT/DT is significantly higher than both % CT/DT and % MT/DT, which suggests that the cognitive load depends on the type of cognitive task that is being performed. Additionally, the significant increase of % DT/DT compared to % CT/DT indicated that there is a cognitive load prior to a motor task. The increased button shifting time and decreased tapping rate in DT may indicate that a residual cognitive load and a concurrent motor load were present.

A Vibrotactile Alarm System for Pleasant Awakening.

There has been a vast development of personal informatics devices combining sleep monitoring with alarm systems, in order to find an optimal time to awaken a sleeping person in a pleasant way. Most of these systems implement auditory feedback, which is not always pleasant and may disturb other sleepers. We present an adaptive alarm system that detects sleeping cycles and triggers alarm signal during shallow sleep, to minimize sleep inertia. Since tactile sensation is associated with positive valence, vibrotactile stimulation is investigated as a silent alarm to enhance pleasant awakening. Three modulation techniques to render the tactile stimuli for pleasant awakening are considered, namely simultaneous, continuous, and successive stimulation. Two experimental studied are conducted. Experiment 1 studied exogenous attention towards tactile stimulation in a multimodal scenario (involving visual and haptic interactions) with fully awake individuals. Results from the attention task and the subjective valence rating suggest that the vibrotactile stimulation should be based on the continuous modulation, since this not only is very perceivable but also associated with positive attention. Experiment 2 evaluated the user experience with tactile stimulation patterns during sleep. Results confirmed the findings of experiment 1. Continuous modulation was rated highest for pleasant yet arousing sleep-awake transition.

Combining Full and Partial Haptic Guidance Improves Handwriting Skills Development.

It has been shown in previous studies that haptic guidance improves the learning outcomes of handwriting motor skills. Full and partial haptic guidance are developed and evaluated in the literature. In this paper, we present two experimental studies to examine whether combining full and partial haptic guidance is more effective for improving handwriting skills than merely full or partial guidance methods. Experiment I, with 22 participants, compares the effectiveness of merely full and partial haptic guidance methods towards improving learning outcomes of Arabic handwriting. Even though haptic guidance in general is found to be effective and pleasant by all participants, experiment I concludes that there are no statistically significant differences in the learning outcomes between full and partial haptic guidance. Experiment II investigates whether a combination of full and partial haptic guidance could further improve the learning outcomes, compared to merely full or partial haptic guidance. The learning outcomes and quality of experience are measured to evaluate each group's performance. Results from experiment II demonstrate that the combination of full and partial haptic guidance results in statistically significant improvements in the quality of handwriting, compared to mere full or partial haptic guidance. In particular, starting with partial haptic guidance at early stage of learning and then using full guidance at intermediate/advanced learning stages seemed to be the most effective. This implies that partial haptic guidance is more effective to learn the gross shape of handwriting skills (at early stages of the learning process) whereas full haptic guidance is more effective to learn the fine details of the handwriting skills (at intermediate or advanced stage of learning). Therefore, partial-then-full haptic guidance seems to be the most effective to improve learning outcomes.