Fun in Fusion Research (Elsevier Insights)

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Neural issues in the control of muscular strength. Res Q Exerc Sport ; 75 :3—8. Mechanisms that contribute to differences in motor performance between young and old adults. J Electromyogr Kinesiol ; 13 :1— Clin Neurophysiol ; — Reliability of spike triggered averaging of the surface electromyogram for motor unit action potential estimation. Muscle Nerve ; 48 — J Neuroeng Rehabil ; 10 Remodeling of the neuromuscular junction precedes sarcopenia related alterations in myofibers. The neuromuscular junction: aging at the crossroad between nerves and muscle.

Front Aging Neurosci ; 6 Neuroscience ; — Ashe J. Force and the motor cortex. Behav Brain Res ; 87 — Synaptic control of motoneuronal excitability. Physiol Rev ; 80 — Decoding the neural drive to muscles from the surface electromyogram. Enoka RM, Duchateau J. Inappropriate interpretation of surface EMG signals and muscle fiber characteristics impedes understanding of the control of neuromuscular function. Gandevia SC. Spinal and supraspinal factors in human muscle fatigue.

Physiol Rev ; 81 — Noninvasive measures of central and peripheral activation in human muscle fatigue. Muscle Nerve Suppl ; 5 :S98— Quantitation of central activation failure during maximal voluntary contractions in humans. Muscle Nerve ; 19 — Merton PA. Voluntary strength and fatigue. Effect of aging on fatigue characteristics of elbow flexor muscles during sustained submaximal contraction. Muscle Nerve ; 39 — Comparative effects of resistance training on peak isometric torque, muscle hypertrophy, voluntary activation and surface EMG between young and elderly women.

Clin Physiol Funct Imaging ; 27 — Motor unit firing rates and contractile properties in tibialis anterior of young and old men. Older adults can maximally activate the biceps brachii muscle by voluntary command. Knee extensor strength, activation, and size in very elderly people following strength training. Muscle Nerve ; 22 — Time to task failure and muscle activation vary with load type for a submaximal fatiguing contraction with the lower leg.

Voluntary muscle activation varies with age and muscle group. Central and peripheral contributions to muscle fatigue in humans during sustained maximal effort. Aging does not affect voluntary activation of the ankle dorsiflexors during isometric, concentric, and eccentric contractions. Motor unit discharge rate following twitch potentiation in human triceps brachii muscle.

Neurosci Lett ; — Knight CA, Kamen G. Adaptations in muscular activation of the knee extensor muscles with strength training in young and older adults. J Electromyogr Kinesiol ; 11 — Quadriceps muscle strength, contractile properties, and motor unit firing rates in young and old men. Muscular performances at the ankle joint in young and elderly men. Are voluntary muscle activation deficits in older adults meaningful? Muscle Nerve ; 27 — Wilder MR, Cannon J. Effect of age on muscle activation and twitch properties during static and dynamic actions.

Older adults exhibit a reduced ability to fully activate their biceps brachii muscle. Reduction in single muscle fiber rate of force development with aging is not attenuated in world class older masters athletes. Decay of force transients following active stretch is slower in older than young men: support for a structural mechanism contributing to residual force enhancement in old age.

J Biomech ; 47 — J Electromyogr Kinesiol ; 23 — Effects of aging and sex on voluntary activation and peak relaxation rate of human elbow flexors studied with motor cortical stimulation. The influence on sarcopenia of muscle quality and quantity derived from magnetic resonance imaging and neuromuscular properties. Age Dordr ; 36 Successful skeletal aging: a marker of low fracture risk and longevity. J Bone Miner Res ; 24 — Balance and skeletal alignment in a group of elderly female fallers and nonfallers. Narici MV, Maffulli N. Sarcopenia: characteristics, mechanisms and functional significance.

Br Med Bull ; 95 — Exerc Sport Sci Rev ; 35 — Kjaer M. Role of extracellular matrix in adaptation of tendon and skeletal muscle to mechanical loading. Physiol Rev ; 84 — When active muscles lengthen: properties and consequences of eccentric contractions. News Physiol Sci ; 16 — J Geriatr Phys Ther ; 30 — Hormone replacement therapy and physical function in healthy older men. Time to talk hormones? Endocr Rev ; 33 — Eur J Endocrinol ; — Differential impact of age, sex steroid hormones, and obesity on basal versus pulsatile growth hormone secretion in men as assessed in an ultrasensitive chemiluminescence assay.

J Clin Endocrinol Metab ; 80 — The influence of estrogen on skeletal muscle: sex matters. Sports Med ; 40 — The Rancho Bernardo Study. Tidball JG. Mechanical signal transduction in skeletal muscle growth and adaptation. Glass DJ. PI3 kinase regulation of skeletal muscle hypertrophy and atrophy. Curr Top Microbiol Immunol ; — Effect of testosterone and a nutritional supplement, alone and in combination, on hospital admissions in undernourished older men and women.

Am J Clin Nutr ; 89 — Sarcopenia: its assessment, etiology, pathogenesis, consequences and future perspectives. J Nutr Health Aging ; 12 — The effects of supraphysiologic doses of testosterone on muscle size and strength in normal men. N Engl J Med ; :1—7. Goodman MN. Tumor necrosis factor induces skeletal muscle protein breakdown in rats. Am J Physiol ; :E—E Proc Soc Exp Biol Med ; — Chronic inflammation alters protein metabolism in several organs of adult rats.

J Musculoskelet Neuronal Interact ; 7 — Age, obesity, and sex effects on insulin sensitivity and skeletal muscle mitochondrial function. Diabetes ; 59 — Aging is an inevitable risk factor for insulin resistance. Journal of Taibah University Medical Sciences ; 1 — Excessive loss of skeletal muscle mass in older adults with type 2 diabetes. Diabetes Care ; 32 — Accelerated loss of skeletal muscle strength in older adults with type 2 diabetes: the health, aging, and body composition study. Diabetes Care ; 30 — J Cachexia Sarcopenia Muscle ; 2 — Proud CG. Regulation of protein synthesis by insulin.

Biochem Soc Trans ; 34 — Endocrinology ; — Genetic influences in sport and physical performance. Sports Med ; 41 — Genet Epidemiol ; 23 — Vitamin D receptor expression in human muscle tissue decreases with age. J Bone Miner Res ; 19 — Ceglia L. Vitamin D and skeletal muscle tissue and function. Mol Aspects Med ; 29 — Clin Endocrinol Oxf ; 73 — Low vitamin D status is associated with reduced muscle mass and impaired physical performance in frail elderly people.

Eur J Clin Nutr ; 67 — J Nutr Health Aging ; 6 — J Musculoskelet Neuronal Interact ; 3 :8— Gerontology ; 60 — Biogerontology ; 11 — Morley JE. Anorexia of aging: a true geriatric syndrome. J Nutr Health Aging ; 16 — Senile anorexia in different geriatric settings in Italy.

Introduction

J Nutr Health Aging ; 15 — Eur J Nutr ; 52 — Undernutrition screening survey in , patients: patients with a positive undernutrition screening score stay in hospital 1. Am J Clin Nutr ; — Eur J Nutr ; 51 — Dietary protein intake in Dutch elderly people: a focus on protein sources. Forum Nutr ; 7 — Nutritional recommendations for the management of sarcopenia. J Am Med Dir Assoc ; 11 — Clin Nutr ; 33 — Am J Clin Nutr ; 87 — Clin Sci Lond ; — Breen L, Phillips SM. Skeletal muscle protein metabolism in the elderly: interventions to counteract the 'anabolic resistance' of ageing. Nutr Metab Lond ; 8 J Am Geriatr Soc ; 57 — Effect of nutritional interventions and resistance exercise on aging muscle mass and strength.

Biogerontology ; 13 — Endocr J ; 60 — J Nutr Health Aging ; 18 — Clin Nutr Effect of oral magnesium supplementation on physical performance in healthy elderly women involved in a weekly exercise program: a randomized controlled trial. Longitudinal Aging Study A. Low vitamin D and high parathyroid hormone levels as determinants of loss of muscle strength and muscle mass sarcopenia : the Longitudinal Aging Study Amsterdam.

J Clin Endocrinol Metab ; 88 — Osteoporos Int ; 20 — Vitamin D status predicts physical performance and its decline in older persons. J Clin Endocrinol Metab ; 92 — Vitamin D and its role in skeletal muscle. Bone ; 47 — Candow DG. The impact of nutritional and exercise strategies for aging bone and muscle. Appl Physiol Nutr Metab ; 33 — Body composition in elderly men: effect of dietary modification during strength training. J Am Geriatr Soc ; 40 — Aust J Physiother ; 52 — Med Sci Sports Exerc ; 43 — J Clin Endocrinol Metab ; 98 — The curious case of anabolic resistance: old wives' tales or new fables?

J Appl Physiol ; — Aging is associated with diminished accretion of muscle proteins after the ingestion of a small bolus of essential amino acids. Am J Clin Nutr ; 82 — Exercise as a remedy for sarcopenia. Balance and aerobic capacity of independent elderly: a longitudinal cohort study. Rev Bras Fisioter ; 15 — The effect of aerobic exercise training on the distribution of succinate dehydrogenase activity throughout muscle fibres.

Can J Appl Physiol ; 23 — Enhanced left ventricular performance in endurance trained older men. Circulation ; 89 — A randomized trial comparing aerobic exercise and resistance exercise with a health education program in older adults with knee osteoarthritis. Physical exercise and sarcopenia in older people: position paper of the Italian Society of Orthopaedics and Medicine OrtoMed. Clin Cases Miner Bone Metab ; 11 — They had a mixed race and were used for evaluation of the Lean UX platform. Each session was 20 min on average.

The data were collected from different devices, e. The results and discussion of evaluations are presented further below. The accuracy of the data acquisition and synchronization process was validated by connected different devices, such as EEG, camera, second-generation Kinect, eye-tracker, and PC with the Lean UX platform cloud.

All data streams from multimodal data sources were acquired, synchronized at server endpoints, and checked for data accuracy using a three-second window size. The rate of missing data packets was used to measure the accuracy of data acquisition module shown in Table 2. The results show a 0. The results are shown in Figure The multimodal data from all devices were effectively synchronized at cloud endpoints in milliseconds. The synchronization module synchronized all sensors, stimuli and API data streams in real-time without manual post-synchronization of data.

For example, the eye-tracker, Kinect, camera, microphone, EEG, and interaction tracker communicated at ms, ms, ms, ms, ms, and ms, respectively, at the first window frame. The synchronization module recognized that all incoming data streams belonged to the single event.

The results depicted that all data streams were well synchronized in a real-time manner, showing the perfection of the synchronization module. Table 3 shows the confusion matrix of automatic facial expressions for Cohn-Kanade dataset. Figure 14 shows the average accuracy for each dataset. The results show a high accuracy for the happy, anger, sadness and surprise, while relatively low accuracy for the fear and disgust. However, generally, the model accuracy was quite reasonable compared to the other video-based emotion recognizers.

There were some challenges for effective emotion recognition for heterogeneous populations with respect to demographic, cultural, and impairment aspects, which can be resolved by improving the landmarking techniques to classify the emotions for face impairment. Audio-based emotion recognition : The result of audio-based emotion metric extraction is shown in Table 4 for Emo-DB dataset. The results show a high accuracy for anger and surprise, while a relatively low accuracy is shown for happy and disgust. Additionally, happy and anger were mixed owing to the high sound pitch, while sadness and neutral were mixed owing to the soft voice.

However, generally, the model accuracy was quite reasonable compared to the other audio-based emotion recognizers. There were some challenges, such as tone differences and voice pitch, which made the audio-based emotion recognition difficult. The results are shown in Figure 15 , where DE achieves a higher accuracy for all frequency bands compared to the other features.

From the experiment and results, we identified that the DE feature is more suitable to fuse with other features of emotion recognizers, such pupil size of the eye tracking data. Average accuracy of the classifier using different features on different frequency bands. Pupil Diameter : We performed different experiments based on the pupil size metric using an eye tracker to observe how the pupil size changed in accordance with different emotional states. From the experiments, we found that the pupil size increased dilated in a sorrowful state, and was smallest in a calm state, as shown in Figure For both positive and negative emotions, the pupil size was larger compared with neutral, which showed a correlation with different emotions.

Emotion fusion : Table 5 shows the average accuracy of emotion fusion. The results show that fusion accuracy is higher than the individual classifier accuracy. The t -test analysis showed no significant differences between the feature level and decision level fusion. The open-ended question analyzer module assessed the affective content sentiment and emotions by using lexicon-based dictionaries; POS-tagging; bag-of-words; and in combination with classifiers, such as SVM or NB.

We used multiple lexicon dictionaries e. For feature selection, filter and wrapper approaches were used for the selection of optimal features that improved the classification accuracy. For the experiment, we used five datasets that are widely used for text-based sentiment analysis.

The results of experiments shown in Table 6 reveal that the ensemble method with minimal feature selection strategies can effectively increase the accuracy of classification compared with the baseline classifier. Understanding user feelings, thoughts, and needs are very important to engaging, sustaining, and increasing the purchase of a product, system, or service.

The UX assessment reveals the user feeling about the product, system, or service and their functionalities. The user may have difficulty expressing their feelings and thoughts about a product, system, or service through traditional methods. Sometimes they may be unable to interpret their own feelings in order to describe them. The physiological measurements in assessing UX can detect emotional arousal and stress, motivation, and visual attention that have direct relationships with user cognitive and affective states in a non-intrusive way.

The mixed-method approach showed importance in the UX evaluation methods by providing more accurate and precise information about the user while interacting with the product.


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However, this approach requires a skilled researcher to integrate multiple devices, synchronize data, analyze data, and make informed decisions. Thus, we developed the Lean UX platform to provide an integrated environment in a seamless manner with real-time synchronization and powerful visualizations. The platform offers plug-and-play support for data collection from different devices and powerful real-time analytics visualization to enable insights of time spans of the user experience with multiple participants.

Further, it helps identify the areas of improvement after assessment of any product, system, or service to improve the overall UX. However, improvements can be made in terms of the classifier performance. Finally, we will increase the datasets for effective emotional state recognition. Table A1 shows the selected items from existing UX questionnaires.

Appendix B depicts the lean UX platform toolkit. The input modalities are dependent on the UX evaluation type. In the survey, we are using User Experience Questionnaire UEQ scale to collect the user experience for measuring the UX, contains six dimension scales such as novelty, stimulation, attractiveness, dependability, and efficiency. While for the other type of UX evaluation, all types of input modalities will be available. The moderator can select any type of input modalities, depend on their study.

After successful creation of the project, the moderator can add tasks to project as shown in Figure A2 and Figure A3. The system will generate automatically the project Id, which is used by the interaction tracker module, to track the user interaction as discussed in Section 4. The moderator first adds the JavaScript code in the header of each page by assigning the project id. The JavaScript code is also responsible to display the feedback form on the completion of the task or error situation. The moderator can collect the UX measurement data by connected the sensors, sensors connectivity is auto checked by the system.

The moderator should add the participant information by adding their name, age, and gender. The moderator can check the different modalities measures such as automatic facial expression analysis, emo voice, interaction tracker analytics e. The participants can express their feeling in both Likert scale and free text format. The self-reported data will be available on the submission of self-reported form by the participant to the moderator.

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The open end question analyzer will analyze the free text self-reported feedback to extract the user sentiment and emotions related to that task shown in Figure A4. This evaluation process will repeat for all participants who will participate in the study for each task. The moderator can check the results of UX evaluation at the task level and project level as shown in Figure A5 and Figure A6. National Center for Biotechnology Information , U. Journal List Sensors Basel v. Sensors Basel. Published online May Find articles by Jamil Hussain. Find articles by Wajahat Ali Khan. Find articles by Taeho Hur.

Find articles by Hafiz Syed Muhammad Bilal. Find articles by Jaehun Bang. Find articles by Anees Ul Hassan. Find articles by Sungyoung Lee.

Author information Article notes Copyright and License information Disclaimer. Received Mar 16; Accepted May Abstract The user experience UX is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners.


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Keywords: user experience evaluation, user experience measurement, eye-tracking, facial expression, galvanic skin response, EEG, interaction tracker, self-reporting, user experience platform, mix-method approach. Introduction The user experience UX is a multi-faceted research area that includes diverse aspects of the experiential and affective use of a product, system, or service [ 1 , 2 ].

Related Work Many approaches have been proposed to acquire the user experience in various ways, including the questionnaire, facial analysis, vocal analysis, biometrics, and others. Observational Measurement Observational measurement is an alternative approach to self-reporting or other methods of measuring user behavior.

Physiological Measurement In this section, we explore different biometric sensors, which obtain physical information as quantifiable data for the UX assessment. Open in a separate window. Figure 1. Figure 2. Figure 3. Figure 4. User Interaction Metrics This module handles the collection of the user interactions and calculating the system performance. We use multimodal data from various sensors, such as eye tracking, for visual attention and EEG for quick detection of emotions, motivations, engagement arousal in the cognitive workload and frustration level.

In this study, we implemented the eye tracking and EEG modules. Automatic facial expression analysis AFEA plays an important role in producing deeper insights in human emotional reactions valence , such as fear, happiness, sadness, surprise, anger, disgust, or neutrality. For AFEA, we used an inexpensive webcam to capture video of a participant in order to reduce the overall financial cost. Our developed AFEA first detects the face in a given video frame or image by applying the Viola Jones cascaded classifier algorithm.

Second, different facial landmarks features are detected e. Finally, the face model is fed into the classifier to provide emotions and facial expression metrics as labels [ 41 ]. Non-verbal gestures i. We will use a depth camera to recognize emotions through user body language in upcoming version of lean UX platform release. The model classifies incoming audio to the platform as seven basic emotions: fear, happiness, sadness, surprise, anger, disgust, or neutrality. The speech signals are divided into frames, then STE detects the energy within each frame for voice segmentation.

Afterward, STZCR is calculated from the rate of change of speech signal within a particular time window. These two features are used to extract the speech segment for emotion recognition and removed the unwanted frames from signals. Subsequently, we have employed the feature level fusion using a set of rules to choose the right emotions as a previous study [ 75 ]. It shows the importance of making a multimodal fusion framework that could effectively extract emotions from different modalities in human-centric environment. The benefit of using multimodal data from different devices is to get deep insights of human emotions and motivations.

Figure 5. Self-Reported Metrics Self-reported metrics [ 35 ] deal with post-tasks that explicitly ask questions about the participant for information about their opinion and their interaction with the system, for example, overall interaction, ease of use, satisfaction, effectiveness, and efficacy. Table 1 A partial list of candidate rules.

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Figure 6. Figure 7. Survey workflow: triangulation of UX metric with self-reporting. Figure 8. Figure 9. Analytics Layer The analytics layer is responsible for providing different analytics based on the UX expert query. Visualization Server UX Toolkit The visualization server is a client application that is used by the UX expert to evaluate the product, system, or service.

Figure Results and Evaluation The proposed platform was evaluated from different aspects, such as multimodal data acquisition error rate, synchronization accuracy, individual UX measurements metrics ranging from interactions, multimodal emotions recognizers, and self-reported assessments.

Multimodal Data Acquisition and Data Synchronization Process The accuracy of the data acquisition and synchronization process was validated by connected different devices, such as EEG, camera, second-generation Kinect, eye-tracker, and PC with the Lean UX platform cloud. Table 2 The data acquisition process accuracy. Table 5 The average accuracies of each classifier and fusion method. Subject Facial Expression Audio Base Textual EEG DE Eye Tracking Fusion Feature Level Decision Level 1 95 84 91 68 80 96 96 2 92 82 89 63 82 97 98 3 80 94 64 83 98 99 4 98 83 89 62 89 93 98 5 98 84 93 76 90 92 93 6 90 83 94 70 81 97 98 7 94 84 94 72 87 91 93 8 93 83 91 69 85 94 94 9 93 80 92 64 80 95 93 10 98 82 92 70 87 98 96 Average Self-Reported Metric The open-ended question analyzer module assessed the affective content sentiment and emotions by using lexicon-based dictionaries; POS-tagging; bag-of-words; and in combination with classifiers, such as SVM or NB.

Table 6 Average accuracies of each classifier for each dataset. Conclusions Understanding user feelings, thoughts, and needs are very important to engaging, sustaining, and increasing the purchase of a product, system, or service. Table A1 The average accuracies of each classifier and fusion method.

Question ID Bipolar Words WL WR 1 annoying enjoyable 2 not understandable understandable 3 dull Creative 4 difficult to learn easy to learn 5 inferior valuable 6 boring exciting 7 not interesting interesting 8 unpredictable predictable 9 slow fast 10 inventive conventional 11 obstructive supportive 12 bad good 13 complicated easy 14 unlikable pleasing 15 usual leading edge 16 unpleasant pleasant 17 not secure secure 18 motivating demotivating 19 Does not meets expectations meet expectations 20 inefficient effient 21 confusing clear 22 impractical practical 23 cluttered organized 24 unattractive attractive 25 unfriendly friendly 26 conservative innovative 27 technical human 28 isolating connective 29 unprofessional professional 30 cheap premium 31 alienating integrating 32 separates me brings me closer 33 unpresentable presentable 34 cautious bold 35 undemanding challenging 36 ordinary novel 37 rejecting inviting 38 repelling appealing 39 disagreeable likeable.

Figure A1. Figure A2. Figure A3. Figure A4. Momentary UX evaluation: real-time data collection and UX metric measurement. Figure A5. Figure A6. Author Contributions J. Conflicts of Interest The authors declare no conflict of interest. References 1. Hassenzahl M. User experience—A research agenda. Liang Y. Sungkyunkwan University; Seoul, Korea: Kula I. Law E.

Roto V. User experience white paper. Bringing clarity to the concept of user experience.

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Laugwitz B. Construction and Evaluation of a User Experience Questionnaire; pp. All About U. Bolger N. Diary methods: Capturing life as it is lived. Karapanos E. Fallman D. Scollon C. Assessing Well-Being. Springer; Dordrecht, The Netherlands: Vermeeren A. Schubert E. A Proposal of collecting Emotions and Experiences. Russel J. Affect grid: A single-item scale of pleasure and arousal. Van Gog T. Uncovering the problem-solving process: Cued retrospective reporting versus concurrent and retrospective reporting.

Goodman E. IEEE Trans. Kuniavsky M. Eye tracking the user experience—An evaluation of ontology visualization techniques. Web J. Springer; Cham, Switzerland: Bojko A. Zheng W. Identifying stable patterns over time for emotion recognition from EEG. ITM Web of Conferences. Volume Liu Y. Let us wish you a happy birthday! Date of Birth. Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Month January February March April May June July August September October November December Year Please fill in a complete birthday Enter a valid birthday.

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