In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The "high throughput" refers to short time at a time of recognition. We investigated various parameters and architectures of the DRNN by using the training dataset of 432 trials with 6 activity classes from 7 people. The maximum recognition rate was 95.42% and 83.43% against the test data of 108 segmented trials each of which has single activity class and 18 multiple sequential trials, respectively. Here, the maximum recognition rates by traditional methods were 71.65% and 54.97% for each. In addition, the efficiency of the found parameters was evaluated by using additional dataset. Further, as for throughput of the recognition per unit time, the constructed DRNN was requiring only 1.347 [ms], while the best traditional method required 11.031 [ms] which includes 11.027 [ms] for feature calculation. These advantages are caused by the compact and small architecture of the constructed real time oriented DRNN.

BackgroundThe prevalence of non-communicable diseases is increasing throughout the world, including developing countries.ObjectiveThe intent was to conduct a study of a preventive medical service in a developing country, combining eHealth checkups and teleconsultation as well as assess stratification rules and the short-term effects of intervention.MethodsWe developed an eHealth system that comprises a set of sensor devices in an attaché case, a data transmission system linked to a mobile network, and a data management application. We provided eHealth checkups for the populations of five villages and the employees of five factories/offices in Bangladesh. Individual health condition was automatically categorized into four grades based on international diagnostic standards: green (healthy), yellow (caution), orange (affected), and red (emergent). We provided teleconsultation for orange- and red-grade subjects and we provided teleprescription for these subjects as required.ResultsThe first checkup was provided to 16,741 subjects. After one year, 2361 subjects participated in the second checkup and the systolic blood pressure of these subjects was significantly decreased from an average of 121 mmHg to an average of 116 mmHg (P<.001). Based on these results, we propose a cost-effective method using a machine learning technique (random forest method) using the medical interview, subject profiles, and checkup results as predictor to avoid costly measurements of blood sugar, to ensure sustainability of the program in developing countries.ConclusionsThe results of this study demonstrate the benefits of an eHealth checkup and teleconsultation program as an effective health care system in developing countries.

Insufficient healthcare facilities and unavailability of medical experts in rural areas are the two major reasons that kept the people unreached to healthcare services. Recent penetration of mobile phone and the demand to basic healthcare services, remote health consultancy over mobile phone became popular in developing countries. In this paper, we introduce two such representative initiatives from Bangladesh and discuss the technical challenges they face to serve a remote patient. To solve these issues, we have prototyped a box with necessary diagnostic tools, we call it a "portable clinic" and a software tool, "GramHealth" for managing the patient information. We carried out experiments in three villages in Bangladesh to observe the usability of the portable clinic and verify the functionality of "GramHealth". We display the qualitative analysis of the results obtained from the experiment. GramHealth DB has a unique combination of structured, semi-structured and un-structured data. We are currently looking at these data to see whether these can be treated as BigData and if yes, how to analyze the data and what to expect from these data to make a better clinical decision support.

Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

As pervasive computing environments become popular, RFID devices, such as contactless smart cards and RFID tags, are introduced into our daily life. However, there exists a privacy problem that a third party can trace user's behavior by linking device's ID.The concept of unlinkability, that a third party cannot recognize whether some outputs are from the same user, is important to solve the privacy problem. A scheme using hash function satisfies unlinkability against a third party by changing the outputs of RFID devices every time. However, the schemes are not scalable since the server needs O(N ) hash calculations for every ID matching, where N is the number of RFID devices.In this paper, we propose the K-steps ID matching scheme, which can reduce the number of the hash calculations on the server to O(log N ). Secondly, we propose a quantification of unlinkability using conditional entropy and mutual information. Finally, we analyze the K-steps ID matching scheme using the proposed quantification, and show the relation between the time complexity and unlinkability.