CityLightSense: A Participatory Sensing-based System for Monitoring and Mapping of Illumination levels

Adequate nighttime lighting of city streets is necessary for safe vehicle and pedestrian movement, deterrent of crime, improvement of the citizens’ perceptions of safety, and so on. However, monitoring and mapping of illumination levels in city streets during the nighttime is a tedious activity that is usually based on manual inspection reports. The advancement in smartphone technology comes up with a better way to monitor city illumination using a rich set of smartphone-equipped inexpensive but powerful sensors (e.g., light sensor, GPS, etc). In this context, the main objective of this work is to use the power of smartphone sensors and IoT-cloud-based framework to collect, store, and analyze nighttime illumination data from citizens to generate high granular city illumination map. The development of high granular illumination map is an effective way of visualizing and assessing the illumination of city streets during nighttime. In this article, an illumination mapping algorithm called Street Illumination Mapping is proposed that works on participatory sensing-based illumination data collected using smartphones as IoT devices to generate city illumination map. The proposed method is evaluated on a real-world illumination dataset collected by participants in two different urban areas of city Kolkata. The results are also compared with the baseline mapping techniques, namely, Spatial k-Nearest Neighbors, Inverse Distance Weighting, Random Forest Regressor, Support Vector Regressor, and Artificial Neural Network.

Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.

Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies

Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, like using video captured by the surveillance cameras or depth image cameras in the lab environment. It also can be recognized by wearable sensors. e.g., accelerometer, force sensors, gyroscope, flexible goniometer, magneto resistive sensors, electromagnetic tracking system, force sensors, and electromyography (EMG). Analysis through these sensors required a lab condition, or users must wear these sensors. For detecting abnormality in gait action of a human, we need to incorporate the sensors separately. We can know about one's health condition by abnormal human gait after detecting it. Understanding a regular gait vs. abnormal gait may give insights to the health condition of the subject using the smart wearable technologies. Therefore, in this paper, we proposed a way to analyze abnormal human gait through smartphone sensors. Though smart devices like smartphones and smartwatches are used by most of the person nowadays. So, we can track down their gait using sensors of these intelligent wearable devices. In this study, we used twenty-three (N=23) people to record their walking activities. Among them fourteen people have normal gait actions, and nine people were facing difficulties with their walking due to their illness. To do the stratification of the gait of the subjects, we have adopted five machine learning algorithms with addition a deep learning algorithm. The advantages of the traditional classification are analyzed and compared among themselves. After rigorous performance analysis we found support vector machine (SVM) showing 96% accuracy, highest among the tradition classifiers. 70%, 84%, and 95% accuracy is obtained by the logistic regression, Naïve Bayes, and k-Nearest Neighbor (kNN) classifiers, respectively. As per the state-of-the art, deep learning classifiers has been proven to outperform the traditional classifiers in similar binary classification problems. We have considered the scenario and applied the 2D convolutional neural network (2D-CNN) classification algorithm, which outperformed the other algorithms showing accuracy of 98%. The model can be optimized and can be integrated with the other sensors to be utilized in the mobile wearable devices.

Smartphone Movement Sensors for Remote Monitoring of Respiratory Rate: Observational Study

BackgroundMobile health offers potential benefits to patients and healthcare systems alike. Existing remote technologies to measure respiratory rate (RR) have limitations, such as cost, accessibility and reliability. Using smartphone sensors to measure RR may offer a potential solution.ObjectiveThe aim of this study was to conduct a comprehensive assessment of a novel mHealth smartphone application designed to measure RR using movement sensors.MethodsIn Study 1, 15 participants simultaneously measured their RR with the app, and an FDA cleared reference device. A novel reference method to allow the app to be evaluated ‘in the wild’ was also developed. In Study 2, 165 participants measured their RR using the app, and these measures were compared to the novel reference. Usability of the app was also assessed in both studies.ResultsThe app, when compared to the FDA-cleared and novel references, respectively, showed a mean absolute error (MAE) of 1.65 (SD=1.49) and 1.14 (1.44), relative MAE of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement (LoA) =-3.27-4.89) and 0.08 (−3.68-3.51). Pearson correlation coefficients were 0.700 and 0.885. 93% of participants successfully operated the app on their first use.ConclusionsThe accuracy and usability of the app demonstrated here show promise for the use of mHealth solutions employing smartphone sensors to remotely monitor RR. Further research should validate the benefits that this technology may offer patients and healthcare systems.

Prescribing Personalized Exercise Programs Using Smartphone Sensors For Remote Fitness Assessment

Guidelines for physical activity emphasize multiple fitness components among people aged >65. The age-related increase in variability of fitness components necessitates accurate individualized assessment prior to optimal prescription for personalized exercise program. Accordingly, we tested feasibility and effectiveness of a novel tool designed to remotely assess balance, flexibility, and strength using smartphone sensors (accelerometer/gyroscope), and subsequently remotely deliver personalized exercise programs via smartphone. This pilot study enrolled 52 healthy volunteers (34 females) aged 65+, with normal cognition and low fall-risk. Baseline preliminary data from smartphone fitness assessment were analyzed to generate 42 fitness digital-markers, used to generate personalized exercise programs (5 times/week for 6 weeks). Programs included graded exercises for upper/lower body, flexibility, strength, and balance (dynamic, static, vestibular). Fitness was remotely assessed at baseline and after six weeks. Average age was 74.7±6.4 years; adherence was 3.6±1.7 exercise sessions/week. Significant improvement for pre/post testing was observed for 10/12 digital-markers of strength/flexibility for upper/lower body (sit-to-stand repetitions/duration; arm-lift duration; torso-rotation; arm-extension/flexion). Balance improved significantly for 6/10 measures of tandem-stance, with consistent (non-significant) trends observed across 20 balance digital-markers of tandem-walk and one leg-stance. Balance showed greatest improvement among the 37 participants exercising ≥3/week. These preliminary results serve as proof of concept among people aged >65: high adherence and improved fitness confirm the potential benefits and niche for remote fitness assessment used to generate personalized exercise programs. Future research is required to confirm the benefits among specific patient groups, such as those with frailty, deconditioning, cognitive and functional impairment.

Smartphones as detector the speed of sound: A classroom explanation and demonstration

This paper presents how smartphones determine the speed of sound (C) with a classroom explanation and demonstration to design a variety of lab instruments. Smartphone sensors such as mics and speakers were used as experimental tools by students for calculating the value of speed of sound. Mathematics is used to describe physics principles using only the mean of repetitive experimental results. After conducting an experiment with 43 students, majoring in general science, faculty of education and educational innovation, Kalasin University, the students report the value of the speed of sound nearly to theoretical values with a percentage difference of less than 0.1%, equipment used in everyday life in the classroom, equipment that is cheap, along with a simple calculation of speed of sound, is an advantage of this experiment.