Search Results (18)

This paper processes the 2015 Benefits of Gender Equality through Infrastructure Provision (BGEIP) Survey, a representative survey for the EU-28, to estimating the impact of everyday life infrastructure access on subjective well-being (SWB) from a gender perspective in Europe. Our estimations prove that accessing everyday life infrastructure in Europe indeed increases SWB, but it contributes to increasing more the SWB of women than that of men. Women’s well-being is positively affected for all kinds of everyday life infrastructures, but the differences with respect to men are larger for the Nursery category for children up to 3 years and for the Centers category for people with long term disabilities. In contrast, men’s well-being is only sensitive to the Health infrastructure and to the Gym and Workout places. Clearly, targeting infrastructure investment helping women in caring children, and other dependents in the family constitute an excellent vehicle for increasing women’s SWB and reducing gender inequality in Europe. Full article

The accurate and efficient recognition of gym workout activities using wearable sensors holds significant implications for assessing fitness levels, tailoring personalized training regimens, and overseeing rehabilitation progress. This study introduces CNN-ResBiGRU, a novel deep learning architecture that amalgamates residual and hybrid methodologies, aiming to precisely categorize gym exercises based on multimodal sensor data. The primary goal of this model is to effectively identify various gym workouts by integrating convolutional neural networks, residual connections, and bidirectional gated recurrent units. Raw electromyography and inertial measurement unit data collected from wearable sensors worn by individuals during strength training and gym sessions serve as inputs for the CNN-ResBiGRU model. Initially, convolutional neural network layers are employed to extract unique features in both temporal and spatial dimensions, capturing localized patterns within the sensor outputs. Subsequently, the extracted features are fed into the ResBiGRU component, leveraging residual connections and bidirectional processing to capture the exercise activities’ long-term temporal dependencies and contextual information. The performance of the proposed model is evaluated using the Myogym dataset, comprising data from 10 participants engaged in 30 distinct gym activities. The model achieves a classification accuracy of 97.29% and an F1-score of 92.68%. Ablation studies confirm the effectiveness of the convolutional neural network and ResBiGRU components. The proposed hybrid model uses wearable multimodal sensor data to accurately and efficiently recognize gym exercise activity. Full article

The prevalence of individuals with flat feet and high arches is very high (between 15% to 37%), which can often lead to other orthopedic complications. Three-dimensional-printed insoles are being studied and validated for their effects in correcting these highly prevalent foot disorders. Highly customizable parameters while printing the insole allows for precise correction of foot biomechanics. In this study, 200 patients suffering from various foot-related problems and joint pain were given 3d-printed insoles (designed using plantar pressure systems and clinical practitioner’s assessment) to use in their footwear. Tested activities included standing, walking, running, sports, and gym workout. Customization of insoles included custom density, heel cup, heel rise, medial arch height, and lateral wedge. Based on the patient history, additional podiatry elements were provided for patients with diabetes. Each insole was designed as per the insole profile of the shoe with a comfortable fit. These insoles were found to be effective in alleviating pain for more than 90% of the patients and provided a longer life cycle with effective orthotic correction (for >16 months of daily use). This paper presents the post-use effects (6–18 months) of custom 3D-printed insoles. Full article

Wearables have been applied in the field of fitness in recent years to monitor human muscles by recording electromyographic (EMG) signals. Understanding muscle activation during exercise routines allows strength athletes to achieve the best results. Hydrogels, which are widely used as wet electrodes in the fitness field, are not an option for wearable devices due to their characteristics of being disposable and skin-adhesion. Therefore, a lot of research has been conducted on the development of dry electrodes that can replace hydrogels. In this study, to make it wearable, neoprene was impregnated with high-purity SWCNTs to develop a dry electrode with less noise than hydrogel. Due to the impact of COVID-19, the demand for workouts to improve muscle strength, such as home gyms and personal trainers (PT), has increased. Although there are many studies related to aerobic exercise, there is a lack of wearable devices that can assist in improving muscle strength. This pilot study proposed the development of a wearable device in the form of an arm sleeve that can monitor muscle activity by recording EMG signals of the arm using nine textile-based sensors. In addition, some machine learning models were used to classify three arm target movements such as wrist curl, biceps curl, and dumbbell kickback from the EMG signals recorded by fiber-based sensors. The results obtained show that the EMG signal recorded by the proposed electrode contains less noise compared to that collected by the wet electrode. This was also evidenced by the high accuracy of the classification model used to classify the three arms workouts. This work classification device is an essential step towards wearable devices that can replace next-generation PT. Full article

Macronutrients play an important role in appetite regulation. In addition, adequate nutrient and energy intake, which may be altered by exercise-induced appetite fluctuations, is required to ensure important training outcomes. However, findings regarding appetite responses to macronutrient consumption before training and to different resistance training intensities remain inconclusive. This study investigated the association of three types of macronutrient intake before different intensities of resistance training with appetite. A purposive cross-sectional design was used to collect data from 280 resistance-trained individuals (mean age 26.4 ± 5.8 years) representing five gyms located in Jbeil, Lebanon, and who completed an online questionnaire. Data collected included socio-demographics, nutritional strategies followed by each respondent, training characteristics, and appetite rating before, during and after exercise using a validated visual analogue scale (VAS). A short-term suppression of appetite was reported during resistance-training, with no significant difference in exercise intensities (p > 0.05). In addition, low-fiber carbohydrate and protein food/beverage content consumed 30–60 min before training had an advantage in appetite suppression. In summary, these findings suggest that resistance training combined with pre-workout consumption of a whole meal was associated with appetite suppression, at least during the short period of exercise. From the perspective of appetite control and energy balance, the critical factor is the quantity and quality of macronutrient food sources, in addition to the timing surrounding training of nutrients ingested. Full article

This paper proposes an indoor location-based augmented reality framework (ILARF) for the development of indoor augmented-reality (AR) systems. ILARF integrates an indoor localization unit (ILU), a secure context-aware message exchange unit (SCAMEU), and an AR visualization and interaction unit (ARVIU). The ILU runs on a mobile device such as a smartphone and utilizes visible markers (e.g., images and text), invisible markers (e.g., Wi-Fi, Bluetooth Low Energy, and NFC signals), and device sensors (e.g., accelerometers, gyroscopes, and magnetometers) to determine the device location and direction. The SCAMEU utilizes a message queuing telemetry transport (MQTT) server to exchange ambient sensor data (e.g., temperature, light, and humidity readings) and user data (e.g., user location and user speed) for context-awareness. The unit also employs a web server to manage user profiles and settings. The ARVIU uses AR creation tools to handle user interaction and display context-aware information in appropriate areas of the device’s screen. One prototype AR app for use in gyms, Gym Augmented Reality (GAR), was developed based on ILARF. Users can register their profiles and configure settings when using GAR to visit a gym. Then, GAR can help users locate appropriate gym equipment based on their workout programs or favorite exercise specified in their profiles. GAR provides instructions on how to properly use the gym equipment and also makes it possible for gym users to socialize with each other, which may motivate them to go to the gym regularly. GAR is compared with other related AR systems. The comparison shows that GAR is superior to others by virtue of its use of ILARF; specifically, it provides more information, such as user location and direction, and has more desirable properties, such as secure communication and a 3D graphical user interface. Full article

Sixty-three men (15–35 years of age) regularly training in Danish gyms and supplement users were interviewed about the use of supplemental protein and food supplements, intake of caffeine- and (-)-epigallocathechin-3-gallate (EGCG)-containing supplements and beverages and any experienced adverse effects. Protein powder (60%), fish oil (54%) and multivitamin/mineral supplements (41%) were the most popular products. The daily supplementary protein intake (mean 0.42 g/kg body weight, users only) in adult men contributed substantially to their protein intake and exceeded the recommended allowance (0.83 g/kg body weight) for six adult participants (14%). Thirty-eight percent of the adult men exceeded the daily caffeine intake presumed to be safe (400 mg) with coffee as the main contributor. Thirty percent drank green tea and among this percentage, two participants had an extreme daily intake (1.5 and 2 -L). EGCG intake could not be estimated from the food supplements due to the lack of label information. Eighteen participants (29%) reported having experienced adverse effects but seventeen did not consult a physician or report the adverse effect to the Danish food authority. The most common adverse effects were insomnia, shaking, headache and palpitations, itching of the skin and stinging. Pre-workout products accounted for 53% of the adverse effects. Three adverse effects came after intake of two brands of supplements known to have contained substances such as 1,3-dimethylamine or derivatives of phenylethylamines previously having caused serious adverse effects. Full article

CrossFit^® has a unique standard for workout of the day for women and men. Scaling is used to set difficulty levels for women in CrossFit^® gyms and competitions. This type of scaling is applied for weightlifting (60–82% of men’s load); however, there are usually no differences in difficulty settings for gymnastics and monostructural metabolic conditioning. Performance analysis is essential for every sports discipline, and statistical data comparing men’s and women’s results from athletics, running, swimming, weightlifting, etc., are available. However, CrossFit^® lacks these statistics. The aim of our study was to analyze how the performances of men and women differed at the 2021 CrossFit Games^®. Our sample comprised 40 female (age 27.8 ± 5.1) and 40 male participants (age 27.2 ± 3.7) competing in the Rx division. Data obtained from all events were analyzed using effect size and percentage. In 14 out of 15 events, men achieved better results than women. Even with the implementation of scaling, women’s results differed by 0.1–33.1% (effect size from small to large). Scaling for women is designed according to general strength and power differences; however, primarily because of anatomic and physiological differences, men attain better results. However, CrossFit Games^® events are always unique, and the events rarely repeat; therefore, our study does not provide firm conclusions. As our study is the first to compare CrossFit Games^® performance between the sexes, further research is needed. Full article

Accurate baseline data are essential for researchers to determine an intervention’s effects yet may be affected by anticipatory anxiety and assessment familiarity. Familiarization sessions help establish accurate baseline data. High-intensity functional training (HIFT) elicits performance outcomes based on constantly varied workouts. It is unclear how familiarization affects anticipatory anxiety and workout performance among HIFT novices. Familiarization was hypothesized to decrease anxiety and improve workout performance. Sixteen college-aged subjects (62.5% women, 20.2 ± 1.14 years) completed one introductory and four sessions of the same workout. All subjects were recreationally trained with no HIFT experience. State and trait anxiety were assessed at the first session. During the workout sessions, state anxiety (SQALS) was assessed upon arrival at the gym (SQALS 1), after learning the workout protocol (SQALS 2), and when the workout concluded (SQALS 3). A significant main effect of the number of previous sessions on workout performance was observed (p = 0.011). A repeated-measures ANOVA showed a main effect of time on SQALS 1 (p < 0.001), SQALS 2 (p < 0.001), and SQALS 3 (p < 0.001). Our results suggest implementing two familiarization sessions for our HIFT-based workout was sufficient to decrease anxiety and establish a baseline measurement. Future research should examine if this remains true for other types of HIFT-based workouts. Full article

Physical activity (PA) is essential for physical, mental, and emotional health; however, few adults engage in enough PA. Group exercise environments such as CrossFit can promote sustained exercise habits through social influence, support, and norms. This cross-sectional study aims to provide evidence for PA social influence at CrossFit. CrossFit members (n = 62) reported PA, workout logging frequency, and members at their gym they: (1) work out with and (2) go to with personal matters. Separate linear network autocorrelation models (LNAMs) determined if individuals reported similar PA scores as those of their social ties at CrossFit that they work out with and/or those they go to for personal matters. Participants reported a mean of 2740.55 MET minutes/week (SD = 1847.08), working out with a mean of 9.89 members (SD = 6.26), and speaking to a mean of 2.66 members about personal matters (SD = 3.68). A person’s PA was significantly associated with that of their ties they go to with personal matters (PE_p = 0.08, SE_p = 0.02), but was not associated with the PA of their ties they work out with (PE_w = 0.02, SE_w = 0.01). Social influence on PA levels was present when a deeper connection is made between members. Fostering and promoting deeper connections between members may help promote PA and continued exercise habits. Full article

The use of mobile fitness apps has been on the rise for the last decade and especially during the worldwide SARS-CoV-2 pandemic, which led to the closure of gyms and to reduced outdoor mobility. Fitness apps constitute a promising means for promoting more active lifestyles, although their attrition rates are remarkable and adherence to their training plans remains a challenge for developers. The aim of this project was to design an automatic classification of users into adherent and non-adherent, based on their training behavior in the first three months of app usage, for which purpose we proposed an ensemble of regression models to predict their behaviour (adherence) in the fourth month. The study was conducted using data from a total of 246 Mammoth Hunters Fitness app users. Firstly, pre-processing and clustering steps were taken in order to prepare the data and to categorize users into similar groups, taking into account the first 90 days of workout sessions. Then, an ensemble approach for regression models was used to predict user training behaviour during the fourth month, which were trained with users belonging to the same cluster. This was used to reach a conclusion regarding their adherence status, via an approach that combined affinity propagation (AP) clustering algorithm, followed by the long short-term memory (LSTM), rendering the best results (87% accuracy and 85% F1_score). This study illustrates the suggested the capacity of the system to anticipate future adherence or non-adherence, potentially opening the door to fitness app creators to pursue advanced measures aimed at reducing app attrition. Full article

Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today’s time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature. Full article

This study aimed to determine the effect of the first English national COVID-19 lockdown on physical activity (PA), sitting time, eating behaviours and body mass in an adult cohort. This was further examined to determine whether conforming to recommended guidelines on PA and sedentary behaviour was improved. Based on an online survey (n = 818) incorporating the International Physical Activity Questionnaire Short Form (IPAQ-SF), self-reported body mass change showed that in 32.2% of participants body mass increased, with 39.1% reporting an increase in food intake. Never exercising at the gym or undertaking an exercise class (online or live), increased by 50.8% during lockdown, with 53.5% changing from exercising frequently to never exercising, suggesting a lack of engagement with online and home workouts. However, outdoor running and cycling >2 times/week increased by 38% during lockdown. Walking at least 30 min continuously on >2 occasions/week increased by 70% during lockdown with minimum 10-min walks on 7 days per week increasing by 23%. The lockdown had a negative impact on sitting time (>8 h a day), which increased by 43.6% on weekdays and 121% at weekends. Furthermore, sitting <4 h/day decreased during lockdown (46.5% and 25.6% for weekdays and weekends, respectively). Those citing tiredness or lack of time as a barrier to exercise reduced by 16% and 60%, respectively, from pre-lockdown to during lockdown. More of the sedentary group met the Public Health England PA recommendations, however most participants still did not meet the UK Government guidelines for PA. Improvements in health per additional minutes of physical activity will be proportionately greater in those previously doing <30 min/week, the area where most improvements were found although, conversely sitting time was greatly increased. This study may assist in informing whether future lifestyle changes could improve the health of the population. Full article

(1) Background: Body Jump^® is a novel group fitness program with musical support, which is performed rebounding in a minitrampoline. Although the number of practitioners has increased exponentially in recent years, this activity’s short-term effects on physical fitness and body composition in women have not yet been studied. (2) Methods: 27 healthy young women were randomly divided into a Body Jump^® group (BJ) and a control group (CG). BJ performed three classes per week for one month. The week before and after the intervention, the anthropometric assessments were carried out to estimate the body composition, and different performance tests were performed to assess the jumping capacity (countermovement jump (CMJ) and squat jump (SJ) tests), the muscular strength (1RM test) and the aerobic fitness (UKK test). (3) Results: VO₂ max (p = 0.001), CMJ flight height (p = 0.023), SJ flight height (p = 0.003) and the 1RM value in the half-squat exercise (p = 0.009) were significantly increased in BJ. In CG, there were no statistically significant differences after the intervention period. Regarding the changes in body composition, a significant enhancement in several parameters were found in BJ, such as the sum of skinfolds (p = 0.003) and the percentage of fat mass (p = 0.002), while no changes were found in any of the anthropometric variables in CG. (4) Conclusions: carrying out the Body Jump^® program three days per week for one month had positive effects on physical fitness and body composition in a group of healthy young women. This training program can be an effective option for enhancing, in the short term, these fitness parameters and the body composition of these recreational users into the fitness centers. Full article

This paper proposes a systemic approach to upper arm gym-workout classification according to spatio-temporal features depicted by biopotential as well as joint kinematics. The key idea of the proposed approach is to impute a biopotential-kinematic relationship by merging the joint kinematic data into a multichannel electromyography signal and visualizing the merged biopotential-kinematic data as an image. Under this approach, the biopotential-kinematic relationship can be imputed by counting on the functionality of a convolutional neural network: an automatic feature extractor followed by a classifier. First, while a professional trainer is demonstrating upper arm gym-workouts, electromyography and joint kinematic data are measured by an armband-type surface electromyography (sEMG) sensor and a RGB-d camera, respectively. Next, the measured data are augmented by adopting the amplitude adjusted Fourier Transform. Then, the augmented electromyography and joint kinematic data are visualized as one image by merging and calculating pixel components in three different ways. Lastly, for each visualized image type, upper arm gym-workout classification is performed via the convolutional neural network. To analyze classification accuracy, two-way rANOVA is performed with two factors: the level of data augmentation and visualized image type. The classification result substantiates that a biopotential-kinematic relationship can be successfully imputed by merging joint kinematic data in-between biceps- and triceps-electromyography channels and visualizing as a time-series heatmap image. Full article