Human activity recognition aims to classify the user activity in various applications like healthcare, gesture recognition and indoor navigation. In the latter, smartphone location recognition is gaining more attention as it enhances indoor positioning accuracy. Commonly the smartphone’s inertial sensor readings are used as input to a machine learning algorithm which performs the classification. There are several approaches to tackle such a task: feature based approaches, one dimensional deep learning algorithms, and two dimensional deep learning architectures. When using deep learning approaches, feature engineering is redundant. In addition, while utilizing two-dimensional deep learning approaches enables to utilize methods from the well-established computer vision domain. In this paper, a framework for smartphone location and human activity recognition, based on the smartphone’s inertial sensors, is proposed. The contributions of this work are a novel time series encoding approach, from inertial signals to inertial images, and transfer learning from computer vision domain to the inertial sensors classification problem. Four different datasets are employed to show the benefits of using the proposed approach. In addition, as the proposed framework performs classification on inertial sensors readings, it can be applied for other classification tasks using inertial data. It can also be adopted to handle other types of sensory data collected for a classification task. Human activity recognition (HAR) aims to classify the user activity in various applications such as gesture recognition ,2, healtFocusing on activity recognition for navigation applications, one of the branches of HAR is smartphone location recognition (SLR). For example, Pocket mode refers to the situation when the smartphone is placed in the user trousers and Swing mode refers to the case where the smartphone is held in the user hand while walking. The SLR goal is to classify the current location of the smartphone on the user. Commonly, both HAR and SLR utilizes the smartphone inertial sensors, namely the accelerometers and gyroscopes, readings to perform the classification task. Both HAR and SLR are gaining more attention in the navigation community. Applying activity recognition in traditional pedestrian dead reckoning (PDR) manged to improve the positioning accuracy ,14,15,16Feature Based: features are extracted from the raw signals of the inertial sensors and used in classical machine learning algorithms.One Dimensional Deep Learning (1D-DL): the raw inertial sensor signals are plugged into one dimensional networks.Two Dimensional Deep Learning (2D-DL): the raw inertial sensors are transformed into two dimensional images and used as input for a network with the same dimensions.Currently, there are three major approaches to tackle HAR or SLR problems:Most of the approaches in the literature are focused on feature based and on 1D-DL networks. As such, there is no need to apply any 1D-2D transformation on the raw data. However, when using 2D-DL networks, the 1D inertial signals are first transformed into 2D space. Thus, compared to 1D-DL, an additional block is required in the algorithm. On the other hand, working with 2D-DL allows the implementation of strong proved architectures and tools derived in the computer vision field.In 2D-DL, besides network architecture and hyper-parameter tuning as in 1D-DL, the major issue is how to transform the 1D inertial signal to a 2D image. The simplest approach is known as raw plots, where all relevant sensor outputs are plotted versus time and the result is used as an image for the 2D-DL classifier. For example, three axes accelerometer data were grouped by columns and the data collected from different positions are grouped by a row in the same image . Unlike In this paper, an Inertial Image (INIM) framework for inertial based, smartphone location and human activity classification is derived. Here, the inertial signals, each represented as a one dimensional vector, are transformed into two dimensional matrices for the classification task. The motivation for this transformation is the ability to utilize strong proved architectures and tools derived in the computer vision field.Encoding. A novel time series encoding approach based on accelerometers and gyroscopes readings. The three-axes accelerometers and three axes gyroscopes signals are encoded into a single RGB image.Transfer Learning. To initialize the backbone deep-learning architecture, transfer-learning is applied form a residual network trained on the ImageNet  using –7) as f as fStepThe Iss2Image encoding technique has three steps:Once the accelerometer signals were transformed to the color images, one can apply any 2D network to perform the classification task.In this section, the proposed INIM framework is addressed. INIM is used for inferring the smartphone location or human activity based on a novel time-series coding method and a backbone 2D Deep Learning Network. INIM framework is illustrated in The purpose of the Mul2Image encoder is to efficiently transform raw accelerometer and gyroscope signals into RGB inertial images. To that end, it is suggested to multiply the inertial sensor readings between themselves in the following manner: accelerometer x-axis readings are multiplied by gyroscopes x-axis readings, and the same for y and z axes. The motivation for such multiplication steams from the pattern recognition domain . There, Step 1: Normalize the specific force vector, k by its three components:Step 2: The normalized accelerometer signals from n epochs are stacked in matrix Step 3: Each angular velocity measurement (gyroscope output) vectorn samples are used to construct the following Step 4: Using (4: Using  and 15)Step 4: UThen, the inertial image, termed INIM, is constructed stacking the three layers:Step 5: The values of  play a major role in computer vision applications. Unlike classical machine learning techniques, in deep learning the net performs representation learning which allows the machine to be fed with raw data and automatically discover the representations needed for the classification task. One of the major challenges in very deep CNN is coping with exploding gradients during the training procedure. To avoid such situations, residual network (ResNet) architecture uses skip connections to enable the gradients to flow easily from a CNN layer to the other. In that manner, the problem of network accuracy degradation is resolved.Therefore, in this work, the ResNet50  network The main building block includes the Mul2Image time series image coding algorithm , the 2D HAR1 [HAR1 . This daHAR2 [HAR2 . In thisHAR3 and HAR4 [and HAR4 . This daSLR [SLR . In thisFour different datasets are used for the evaluation of the proposed approach and comparison to other approaches. All those datasets contain accelerometers and gyroscopes measurements, each labeled with a specific activity label. Three datasets consist of HAR activities and one with SLR as follows:These datasets were chosen as they are commonly used for baseline benchmarking of HAR and SLR problems and all of them were constructed for evaluating deep-learning approaches. In HAR1, HAR2 and HAR3 focused was given to dynamics related human activities, The difference between the three datasets is the inertial sensor locations and their type, sampling rate and different people who made the recordings. The activity list of each dataset is summarized in After choosing the datasets, the corresponding inertial images and their patches were constructed following the steps described in Note that each inertial image is divided into smaller patches based on the network input size as mentioned in The overall numbers, per activity and dataset are shown in As presented in For each dataset, as described in The model was trained with a mini-batch of size 24 and optimized using adaptive moment estimation (Adam)  algorithThe network was run for 10 epochs, 148 iterations for training and 50 for validation. The model coding was implemented using Matlab, and trained on a single NVIDIA GeForce GTX 1080 GPU. This end-to-end process was repeated three times to check the stability of the results.The proposed approach is compared to Iss2Image solution, which is considered a state-of-the-art method in image construction from inertial measurements. The proposed approach uses both accelerometer and gyroscope measurements, while Iss2Image uses only the former. Thus, to make a fair comparison the Iss2Image approach was elaborated to include gyroscopes measurements as well. To that end, instead of working with an image patch with size 224 × 3 × 3 pixels, the extended Iss2Image (eIss2Image) now works with 224 × 6 × 3 pixels. In the proposed approach the image patch has the size of 224 × 224 × 3 pixels, that is approximately 37 times more pixels in the patch. As a consequence, the time required to create the image patches in the proposed approach is larger than the two versions of Iss2Image. For example, the time required to construct ten image patches for the Iss2Image is 0.25 s while for Mul2Image 0.39 s, that is about 64% faster than the proposed encoder. The image patches and average time to construct ten image patches for each approach are given in SLR classification task is considered. For a fair comparison between the proposed Mul2Image encoder and the one used in Iss2Image, the same backbone network, that is ResNet50, was used for all the encoders. In addition, the 2D architectures are also compared to a 1D deep-learning CNN based network as used in .As the test dataset is balanced and the accuracy of each class is high, The HAR classification task is considered using the four datasets with different classes as described in In Comparing the bias metric of Mul2Image method in both To summarize the results of Human activity recognition is an important task in various applications like healthcare, gesture recognition and indoor navigation. In the latter, smartphone location recognition is gaining more attention as a critical operation as it enhances indoor positioning accuracy. In this paper, a framework for both human activity and smartphone location recognition, based on the smartphone’s inertial sensors, was proposed. This framework, termed INIM for inertial images, transforms the accelerometer and gyroscope signals into images enabling the usage of proved architectures and tools from the computer vision domain.The main contributions of this work are a novel time series encoding approach, from inertial signals to inertial images, Mul2Image, and demonstrating transfer learning from computer vision domain (using ImageNet) to the inertial based classification of HAR and SLR problems.To evaluate the proposed approach four different datasets, contain 13 different labels, were employed. Performance (in terms of accuracy) was compared relative to the original Iss2Image approach and also to an extension of this approach that the usage of gyroscopes measurements in the encoding process. To make a fair comparison between those three encoders, the same backbone ResNet50 was employed. In addition, the SLR task, performance was compared also to a leading 1D-CNN architecture. Results show that the proposed extension of the Iss2Image encoder obtained better performance than the original Iss2image approach. The proposed Mul2Image encoder, obtained the overall best accuracy of 88.6% improving Iss2image accuracy by 7.8% and eIss2image by 4.7%.In addition, the INIM framework or its Mul2Image encoder, can be applied to any other classification tasks using inertial sensors data. Moreover, it can also be adopted to handle other types of sensory data collected for any type of a classification task.


Dry January is a national multimedia campaign in the UK that encourages people to abstain from drinking alcohol during the month of January. The population-level campaign makes extensive use of email and social media to support participants and has reported a substantial increase in participation since 2015. This study aimed to assess whether the increase in participation in Dry January between 2015 and 2018 was associated with reduced alcohol consumption in England.Repeat cross-sectional design.England, March 2014 to January 2018.A total of 37,142 respondents to the Alcohol Toolkit Study, a monthly in-home survey of alcohol consumption among representative cross-sectional samples of people aged 16+ years in England.Outcomes included i) percentage of adults reporting drinking monthly or less frequently in the last 6 months and ii) mean weekly alcohol consumption among drinkers derived from the Alcohol Use Disorders Identification Test questions on typical frequency and quantity in the last 6 months.For each outcome, regression models were fitted for month: January (2015 and 2018) vs March-December (2014 and 2017) and for year: 2014/15 vs 2017/18. Interaction terms were included in the models to examine whether the difference between January and the preceding months on each outcome measure depended upon the year (2014/15 vs 2017/18). For non-significant interactions, Bayes factors were calculated to assess the relative strength of evidence for large effects (OR = 1.80 on monthly drinking and β=-1.0 on mean consumption) compared with the null.Differences between January and other months were similar in 2014/15 and 2017/18 for adults reporting drinking monthly or less frequently and the mean consumption among drinkers .The increase in participation in Dry January between 2015 and 2018 was not associated with large corresponding changes in people drinking monthly or less frequently over the last 6 months, or in mean weekly consumption among drinkers. It aims to reduce alcohol consumption by enabling people to ‘take control’ of their drinking, which includes reducing the quantity and frequency of drinking, and by encouraging conversations by the public about reducing alcohol consumption .Dry January launched in 2013 in the UK and has gained popularity . AlcoholIncreased participation has been accompanied by an increase in media coverage and social media engagement, which has been attributed to Public Health England’s collaboration with Alcohol Concern in 2015 to enhance diffusion of the campaign . This upCohen’s d = .53), drinks per typical drinking day (d = .25) and frequency of drunkenness (d = .40) 6-months after the end of Dry January. However, the authors found similar reductions for participants who did not complete Dry January. An evaluation of Febfast 2011 in Australia suggested participants reduced their alcohol consumption compared with the general population in the short-term  is a monthly, cross-sectional, nationally representative in-home survey of adults aged 16+ in England living in private households. Data on the prevalence of at-risk drinking, at-risk drinker attempts to reduce consumption, methods used to reduce consumption and the receipt of advice from healthcare professionals are collected alongside sociodemographic and smoking information . This st2.2Each month, approximately 1700 adults aged 16+ in England are interviewed. Interviews are conducted mid-month (weeks two or three). Sampling is a hybrid between random location and quota sampling, details of which can be found in the ATS protocol . This st2.32.3.1(i)Month: Data collection months were dichotomised into January vs. non-January (March-December of the preceding year). February was excluded to mitigate the effect of participants reporting January activity in February when such activities may still be salient. Whilst this may result in December’s activity being reported in January, using January data was still considered the best way of assessing January activity.(ii)Year: Data collection year was dichotomised into 2014/15 (Dry January 2015) and 2017/18 (Dry January 2018).2.3.2alcohol you have drunk in the last 6 months’ but have demonstrated responsiveness to short-term changes ((i)Percentage of all adults reporting drinking monthly or less frequently.The ATS includes the 3-item consumption questions from the Alcohol Use Disorders Identification Test (AUDIT-C) . The que changes . This as‘How often do you have a drink containing alcohol?: 1 (Never), 2 (Monthly or less), to 6 (6 or more times per week)’. Scores of 1 and 2 were recoded as ‘monthly or less frequently’.(ii)Mean weekly alcohol consumption among adults who report drinking.This was assessed using AUDIT-C question 1: ‘How often do you have a drink containing alcohol?’ and 2: ‘How many standard drinks containing alcohol do you have on a typical day when you are drinking?: 1 (1-2) to 7 (16+)’. One standard drink was equivalent to one UK alcohol unit and the number of units contained in a range of drinks was included to help participants calculate their consumption. In line with previous research Percentage of at-risk drinkers reporting a current attempt to restrict alcohol consumption.We planned to report outcomes related to attempts to cut down, which are assessed only among at-risk drinkers (classified as scoring ≥5 on the AUDIT-C). These outcomes could only reflect increased participation in Dry January if either i) Dry January affected attempts but did not translate to changes in consumption or ii) the consumption questions were not sufficiently responsive to short-term change. Otherwise, recent consumption changes would lead to Dry January participants not being classified as at-risk drinkers. Therefore, we included the following additional measures to assess the effects of Dry January under the alternative assumption that consumption did not change or that consumption changes could not be detected using AUDIT-C.Are you currently trying to restrict your alcohol consumption? (Yes/no)’.(ii)Percentage of at-risk drinkers citing Detox  as a motive in their most recent attempt to restrict alcohol consumption.This outcome was assessed by the question ‘Which of the following, if any, do you think contributed to you making the most recent attempt to restrict your alcohol consumption?’(iii)Percentage of at-risk drinkers reporting use of a website or app to help to restrict alcohol consumption in their most recent attempt.Those reporting an attempt to restrict alcohol consumption in the past 12 months (including current attempts) were asked: ‘‘Which, if any, of the following did you use to try to help restrict your alcohol consumption during the most recent attempt?’ With the option: ‘Visited a website for help with drinking/used an alcohol application (‘app’) on a handheld computer…)’Those reporting an attempt to restrict alcohol consumption in the past 12 months (including current attempts) were asked 2.4Logistic regression analyses with interaction terms were conducted with the dichotomous outcomes  as a motive in their most recent attempt to restrict alcohol consumption, percentage of at-risk drinkers reporting us of a website or app to help restrict alcohol consumption in their most recent attempt) as the dependent variables, and a linear regression with the continuous consumption outcome  as the dependent variable. Month (Jan vs Mar-Dec) and year (2017/18 vs 2014/15) were the independent variables. Where interaction effects were identified, the analysis was stratified by year to estimate the simple effect. Bayes factors were calculated to explore whether non-statistically significant findings could be better explained by data that were insensitive (i.e. unable to differentiate between the hypothesis and the null hypothesis) or whether the data supported the null hypothesis of no interaction between year and month.Data were weighted using a rim  weighting technique to match the English population. The validity of the weighting was explored in a sensitivity analysis that repeated the analysis on unweighted data adjusted for socio-demographic characteristics . The pattern and direction of results were largely similar for analyses of weighted and unweighted data.http://www.lifesci.sussex.ac.uk/home/Zoltan_Dienes/inference/Bayes.htm).All analyses excluding the calculation of Bayes factors were conducted in STATA 15.0. Bayes factors were produced using an online calculator  and 2017/18  (January 2015) to 5.7 units (SD 10.3)  with the outcomes related to alcohol consumption, it is useful to assess the extent to which there was an association between activities among at-risk drinkers, which may not have translated to changes in consumption .Of the total sample, 26 % were at-risk drinkers (scoring ≥5 on AUDIT-C). The percentage of at-risk drinkers reporting a current attempt to restrict consumption was higher in January than non-January months in both 2014/15 (25 % vs 20 %) and 2017/18  as a motive in their most recent attempt to restrict consumption was higher in January than non-January months in both 2014/15 (13 % vs 4%) and 2017/18  as a motive in their most recent attempt to restrict consumption were significantly higher in January vs non-January  and significantly higher in 2017/18 vs 2014/15 . The interaction between month and year was significant , with the odds of citing Detox  being significantly higher in January vs non-January in 2014/15  vs 2017/18 .The percentage of at-risk drinkers citing 3.5The percentage of at-risk drinkers reporting use of a website/app to help in their most recent attempt was the same in January vs non-January months in 2014/15 (2%) and similar in January vs non-January in 2017/18 . The data were insensitive on whether there were large corresponding changes in reported attempts to cut down consumption among at-risk drinkers or reported use of a website or app to support recent attempts to restrict consumption. The odds of citing Detox  as a motive in a recent quit attempt were greater in January than other months but the difference was Previous studies of Dry January and other alcohol abstinence initiatives have indicated participation may be associated with reduced consumption . These oSimilar to previous studies, attempts to reduce alcohol consumption were higher in January compared to other months . The incDetox (e.g. Dry January) as a motive was greater in January compared with other months as expected. However, the association was greater in the 2014/15 (when Dry January was said to have had lower participation) than in the most recent year when participation in Dry January was reportedly higher. This seemed to reflect the proportion of people citing Detox in the rest of the year growing more quickly (4 % in 2014/15 compared with 11 % in 2017/18) than it did in January (13 % vs 18 %). This may be a spill over effect from increased coverage of Dry January, Dryathlon and Go Sober for October leading people to ‘detox’ throughout the year. One possible mechanism is through people being empowered to stop drinking for periods throughout the year . Alternefficacy , 2020 to4.1Alcohol consumption was measured using the self-reported AUDIT-C, which asked respondents to consider their consumption in the last six months. However, the AUDIT-C is a validated tool and responsive to short-term change . Our finonly assessed in at-risk drinkers. If Dry January caused people who would have been classified as at-risk drinkers in December to substantially reduce their consumption, then a proportion of these people would not have been classified as at-risk drinkers when surveyed in January and their attempts would not have been assessed. However, the current study found evidence of no association between increased participation and large reductions in consumption, nor did it find large reductions in consumption reported in January across all years, suggesting it is unlikely a large number of Dry January participants were missed.Another limitation is that attempt-related outcomes were A further limitation is that mean consumption was estimated among drinkers only. If a large proportion of people who drank heavily before January reported non-drinking in our January surveys, then mean consumption among drinkers – rather than all people – would underestimate the impact of Dry January. We considered adding a sensitivity analysis assessing consumption among all adults. However, having established that the proportion of non-drinkers did not increase in January – and in fact decreased – it was judged unnecessary because this decrease suggests any underestimation was unlikely.The ATS did not collect data before the first Dry January campaign and it was therefore only possible to compare data from two time points at which Dry January was thought to have lower (2014/15) and higher (2017/18) participation. Therefore, findings do not relate to any overall impact of Dry January.Detox (e.g. Dry January)’ included people undertaking a detox for any reason. A change in the focus of Dry January away from ‘detox’ over time would have undermined the ability to detect a true increase in Dry January being cited as a motive. However, we are not aware of such a focus shift.The measure relating to whether people cited ‘The analysis did not take account of any UK policy changes within the period studied; however, we would argue that has been very little substantive change to UK alcohol policy within this period.The measures relating to detox and use of a website or app referred to current or most recent attempt in the last 12 months. This means that the reports in January may have related to activity from previous months. Although this would have reduced the sensitivity of the measure, everything else being equal, increases in January over and above other months, should still have been detected by this measure.Finally, whilst we consider a population-level change in alcohol a key indicator of the success of the national Dry January campaign, such changes are not necessarily the outcomes by which the campaign organisers would judge success.4.2Detox (e.g. Dry January) as a motive between 2015 and 2018 but the increased odds of citing the motive in January compared with non-January months was greater in 2014/15 than in 2017/18, which is inconsistent with a large increase in participation. Future evaluations might take a broader systems perspective . SPHR is a partnership between universities. Data collection for the Alcohol and Smoking Toolkit Studies were supported by 10.13039/501100000289Cancer Research UK (C1417/A22962). The views expressed are those of the authors(s) and not necessarily those of the NHS, NIHR, or Department of Health. No funders had any involvement in the design of the study, the analysis or interpretation of the data, the writing of the report, or the decision to submit the paper for publication. JB is a member of the SPECTRUM consortium, supported by the UK Prevention Research Partnership (MR/S037519/1). PC is funded by the Medical Research Council’s Doctoral Training Programme.The Alcohol Toolkit Study receives funding from the PC led on creating the protocol, completing the analysis and writing the first draft on the manuscript with support from JB. All named authors were involved in the development of the research protocol and in reviewing and amending the manuscript.The authors report no declarations of interest.


EWSR1–AFTF1 rearrangement was detected by fluorescence in situ hybridization and further confirmed by whole-transcriptome sequence analysis. The patient had pulmonary metastasis 8 months later and soon died of the disease. The overall survival of the patient was 20 months. In summary, we reported an extremely rare case of cardiac GNET, indicating that the location of GNET should not be confined to the GI tract as initially defined. Due to the lack of a specific effective treatment and the occurrence of early metastasis, cardiac GNET conferred a poor prognosis. More clinical and experimental studies are warranted to better manage this disease in the future.Malignant gastrointestinal neuroectodermal tumor (GNET) is an extremely rare soft tissue sarcoma and has been designated as a new entity recently. At present, GNET virtually exclusively occurs in the gastrointestinal tract. Here we report a case of extra-GNET that arose in the right heart. A 62-year-old male complained of chest distress and breathing difficulty while lying down at night for over 1 month at admission. The radiological findings revealed an occupying lesion involving the right atrium and the right ventricle without any abdominal abnormalities. The patient then underwent a surgical resection. Microscopically, neoplastic cells proliferated in the pattern of nests and sheets with fibrous separation. Focal areas with cellular dyscohesion imparted a vague pseudopapillary pattern. These tumor cells were small to medium in size with fine chromatin and predominantly pale eosinophilic cytoplasm. The nuclei were typically round to oval with somewhat irregular contours and contained small nucleoli. The mitotic figures were easily found. Immunohistochemically, the neoplastic cells were positive for S100 and SOX-10 but negative for HMB-45, A103, and CD99.  Malignant gastrointestinal neuroectodermal tumor (GNET), a synonym for clear cell sarcoma (CCS)-like tumor of the gastrointestinal (GI) tract, is a very rare mesenchymal tumor first described by Zambrano et al. . It is uvia the coronary sinus, forming another lesion of 4 × 3 × 3 cm that was later removed.A 62-year-old male was admitted to our hospital complaining of chest distress and breathing difficulty while lying down at night for over 1 month. He had a medical history of hypertension and denied any specific family history. On physical examination, no heart murmurs or vascular abnormalities were audible with the heart rate of 66 beats per minute. Pulse deficit was not found as well. The abnormal laboratory findings included a modestly raised level of CA125 (36.10 U/ml) and a remarkably elevated ferritin . The brain natriuretic peptide level was within the normal range. Unfortunately, details on troponin and myohemoglobin levels were unavailable. An electrocardiogram indicated left ventricular hypertrophy and complete right bundle branch block. A computed tomography (CT) scan showed an occupying lesion involving the right atrium and the right ventricle  without in situ hybridization (FISH) preformed on paraffin-embedded mass tissues identified EWSR1–ATF1 rearrangement by EWSR1 break-apart probe  and angiosarcomas . To our EWSR1–ATF1/CREB1 fusions, and it can arise at any location in the GI tract, with the small intestine (particularly ileum) being the most common , as IS often demonstrates MDM2 amplification and a negative expression of S100 or SOX10  should also be considered for differentiation due to a similar expression of Syn and CD56 and morphology to GNET. However, ES/PNET often expresses CD99 protein, which was absent in this case, and frequently harbors EWSR1-Fli1 gene fusion . The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.ZL, XP, and LH analyzed and interpreted the data and contributed to the writing of the manuscript. YF and LL collected all the clinical and pathological data. YX and WG collected and edited the images. XF revised the final manuscript. All the authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.