Trouser Pocket Research Articles

An IoT based device-type invariant fall detection system

As the world elderly population is increasing rapidly, the use of technology for the development of accurate and fast automatic fall detection systems has become a necessity. Most of the fall detection systems are developed for specific devices which reduces the versatility of the fall detection system. This paper proposes a centralized unobtrusive IoT based device-type invariant fall detection and rescue system for monitoring of a large population in real-time. Any type of devices such as Smartphones, Raspberry Pi, Arduino, NodeMcu, and Custom Embedded Systems can be used to monitor a large population in the proposed system. The devices are placed into the users’ left or right pant pocket. The accelerometer data from the devices are continuously sent to a multithreaded server which hosts a pre-trained machine learning model that analyzes the data to determine whether a fall has occurred or not. The server sends the classification results back to the corresponding devices. If a fall is detected, the server notifies the mediator of the user's location via an SMS. As a failsafe, the corresponding device alerts nearby individuals by sounding the buzzer and contacts emergency medical services and mediators via SMS for immediate medical assistance, thus saving the user's life. The proposed system achieved 99.7% accuracy, 96.3% sensitivity, and 99.6% specificity. Finally, the proposed system can be implemented on a variety of devices and used to reliably monitor a large population with low false alarm rate, without obstructing the users’ daily living, as no external connections are required.

VALIDITY AND RELIABILITY OF REMOTE, SMARTPHONE-BASED ASSESSMENT OF DUAL-TASK STANDING AND WALKING IN OLDER ADULTS

Dual task walking assessments provide valuable insights into cognitive-motor function in aging. To date, such assessments have been limited primarily to laboratory-based settings. We thus created a smartphone App utilizing multi-media instructions and the phone’s motion sensors to record movements during normal and dual task walking, with the phone placed in the user’s pants pocket. Thirty younger and older adults completed two lab visits, during which walking data were simultaneously acquired by the App and the GAITRite mat. Participants also completed App-based assessments in their homes on three separate days. Across all detected strides in laboratory trials, gait metrics derived from the App correlated closely with those derived from the GAITRite mat (r2>0.96). Across trials, gait metrics demonstrated excellent test-retest reliability, both within and between laboratory visits and home-based assessments (ICC: 0.79–0.90). Remote, smartphone-based dual task walking assessments may therefore be feasible for relatively healthy younger and older adults.

NOVEL REMOTE ASSESSMENT OF THE STANDING POSTURAL CONTROL IN YOUNGER AND OLDER ADULTS USING SMARTPHONE APPLICATION

In older adults, assessment of standing postural control under various task and/or environmental conditions provides valuable insight into cognitive-motor function. To date, however, such assessments have been limited primarily to laboratory or clinical settings. We therefore created a smartphone App to enable remote assessments of postural control. This App provides users with standardized multi-media instructions and harnesses the phone’s internal motion sensors to capture postural sway, with the phone placed in the user’s pants pocket, during trials of standing with eyes open, eyes-closed, and while performing serial-subtractions (i.e., dual tasking). We then established the test-retest reliability of several metrics of postural sway derived from this assessment tool, as well as their sensitivity to the effect of age and standing condition. Fifteen healthy younger and 15 older adults completed multiple standing trials in two separate laboratory visits and on three separate days in their own homes. Postural sway metrics included the mean distance from the center of the trajectory and root mean square were derived from both transverse-plane acceleration and angular velocity time series. Each sway metric demonstrated excellent test-retest reliability, even when analyzed separately by group and standing condition (ICCs: 0.78-0.89). Moreover, each metric was sensitive to age group and standing condition, such that greater sway was observed in older adults as compared to younger adults (p<0.03), and in more challenging standing conditions (p<0.0001). These results suggest that sensitive metrics of standing postural control may be reliably obtained from remote smartphone-based assessments in both younger and older adults.

Klasifikasi percepatan dari sinyal gempa bumi dan sinyal linier aktivitas manusia menggunakan akselerometer smartphone berbasis algoritme support vector machine

The threat of earthquake calamity spread throughout most of the Indonesian archipelago. Smartphone’s accelerometer usage as a seismic parameter detector in Indonesia, of which the noise has obstacles, mainly due to human activities. This study aims to classify linear acceleration signals caused by human activity and earthquake acceleration signals as an initial effort to reduce noise caused by human activity in the smartphone’s accelerometer signals. Both signals are classified by using the Support Vector Machine (SVM) algorithm of which consists of several steps, respectively, data collection, data preprocessing, data segmentation, feature extraction, and classification. These algorithms are tested to 2545 human activity signals in trouser pocket, 2430 human activity signals in shirt pocket and earthquake acceleration signals. Based on the test results by using the confusion matrix, linear acceleration signal data caused by human activity and earthquake acceleration signals can be classified properly using an SVM algorithm with Polynomial or Gaussian kernel with a small kernel scale value. The algorithms can achieve an accuracy of 87.74% to 97.94%.

Detection of body postures and movements in ambulatory adults with cerebral palsy: a novel and valid measure of physical behaviour

BackgroundAccurate measurement of physical behaviour is paramount to better understand lifestyle, health, and functioning, particularly in adults with physical disability as they may be at higher risk of sedentary lifestyle and subsequent negative health consequences. This study aimed: 1) to evaluate the criterion validity of a novel and clinically applicable activity monitor (AM, Activ8), in the detection of body postures and movements in adults with spastic cerebral palsy (CP); and 2) to evaluate the extent that the AM’s positioning affects validity.MethodsIn this cross-sectional study, 14 ambulatory adults with CP [9 men; mean (SD) age, 35.4 (13.1) years] performed standardized activities while wearing three Activ8 monitors - frontolateral thigh (primary position), frontal thigh, and pant pocket - and being video recorded (criterion measure). AM activity output was compared to synchronized video recordings. Absolute (seconds) and relative [(video time–AM time)/mean time, %] time differences between methods were calculated. Relative time differences of < 10% were indicative of good validity. Comparison of AM attachment positions was completed using Spearman Rho correlation coefficients and Meng’s tests.ResultsCriterion validity of the AM (frontolateral thigh) was good (average relative time differences: 0.25% for sitting, 4.69% for standing, 2.46% for walking, 1.96% for upright activity, 3.19% for cycling), except for running (34.6%). Spearman Rho correlation coefficients were greater between video/frontolateral thigh position than video/frontal thigh position and video/pant pocket position for body posture and movement categories sitting, standing, walking, and upright activity (p < 0.01 for all).ConclusionsThe AM, positioned on the frontolateral thigh, demonstrated good criterion validity in ambulatory adults with CP. Though the Activ8 offers potential as an objective measure of physical activity, appropriate positioning is paramount for valid measurement.

Accuracy of Physical Activity Monitors for Steps and Calorie Measurement During Pregnancy Walking

Purpose: This purpose of this study was to validate consumer-based and research-grade PA monitors for step counting and Calorie expenditure during treadmill walking. Methods: Participants (n = 40, 24 in second trimester and 16 in third trimester) completed five 2-minute walking activities (1.5–3.5 miles/hour in 0.5 mile/hour increments) while wearing five PA monitors (right hip: ActiGraph Link [AG]; left hip: Omron HJ-720 [OM]; left front pants pocket: New Lifestyles NL 2000 [NL]; non-dominant wrist: Fitbit Flex [FF]; right ankle: StepWatch [SW]). Mean absolute percent error (MAPE) was used to determine device accuracy for step counting (all monitors) and Calorie expenditure (AG with Freedson equations and FF) compared to criterion measures (hand tally for steps, indirect Calorimetry for Calories). Results: For step counting, the SW had MAPE ≤ 10% at all walking speeds, and the OM and NL had MAPE ≤ 10% for all speeds but 1.5 miles/hour. The AG had MAPE ≤ 10% for only 3.0–3.5 miles/hour speeds, and the FF had high MAPE for all speeds. For Calories, the FF and AG had MAPE &gt; 10% for all speeds, with the FF overestimating Calories expended. Trimester did not affect PA monitor accuracy for step counting but did affect accuracy for Calorie expenditure. Conclusion: The ankle-worn SW and hip-worn OM had high accuracy for measuring step counts at all treadmill walking speeds, whereas the NL had high accuracy for speeds ≥2.0 miles/hour. Conversely, the monitors tested for Calorie expenditure have poor accuracy and should be interpreted cautiously for walking behavior.

Risks to Health and Well-Being From Radio-Frequency Radiation Emitted by Cell Phones and Other Wireless Devices.

Radiation exposure has long been a concern for the public, policy makers, and health researchers. Beginning with radar during World War II, human exposure to radio-frequency radiation1 (RFR) technologies has grown substantially over time. In 2011, the International Agency for Research on Cancer (IARC) reviewed the published literature and categorized RFR as a “possible” (Group 2B) human carcinogen. A broad range of adverse human health effects associated with RFR have been reported since the IARC review. In addition, three large-scale carcinogenicity studies in rodents exposed to levels of RFR that mimic lifetime human exposures have shown significantly increased rates of Schwannomas and malignant gliomas, as well as chromosomal DNA damage. Of particular concern are the effects of RFR exposure on the developing brain in children. Compared with an adult male, a cell phone held against the head of a child exposes deeper brain structures to greater radiation doses per unit volume, and the young, thin skull's bone marrow absorbs a roughly 10-fold higher local dose. Experimental and observational studies also suggest that men who keep cell phones in their trouser pockets have significantly lower sperm counts and significantly impaired sperm motility and morphology, including mitochondrial DNA damage. Based on the accumulated evidence, we recommend that IARC re-evaluate its 2011 classification of the human carcinogenicity of RFR, and that WHO complete a systematic review of multiple other health effects such as sperm damage. In the interim, current knowledge provides justification for governments, public health authorities, and physicians/allied health professionals to warn the population that having a cell phone next to the body is harmful, and to support measures to reduce all exposures to RFR.

Method based on UWB for user identification during gait periods

Everyone has a different way of walking, and for this reason, gait has been studied in the last few years as an important biometric information source. This study explores a novel approach, based on ultra-wideband (UWB) technology, for user identification via gait analysis. In the proposed method, the user is supposed to wear two or more devices embedding a UWB transceiver. During gait, the distances between the devices are estimated via UWB and then analysed by means of a machine learning classifier, which provides automatic identification. Experiments were carried out by 12 volunteers, who walked while wearing four UWB boards (placed on the head, wrist, ankle, and in a trouser pocket). The off-line evaluation considered a set of different possible configurations in terms of number and position of the wearable devices. Despite a relatively low sampling frequency of 10 Hz, the results are promising: average identification accuracy is as high as ∼96% with four devices, and above 90% with three devices (wrist, trouser pocket, and ankle). This novel approach may enhance the accuracy of inertial-based systems for continuous user identification.

Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases.

Wearable sensors may enable the continuous monitoring of gait out of the clinic without requiring supervised tests and costly equipment. This paper investigates the use of a single wearable accelerometer to detect foot contact times and estimate temporal gait parameters (stride time, swing and stance duration). The experiments considered two possible body positions for the accelerometer: over the lower trunk and inside a trouser pocket. The latter approach could be implemented using a common smartphone. Notably, during the experiments, the ground truth was obtained by using a pair of sensorized shoes. Unlike ambient sensors and camera-based systems, sensorized shoes enable the evaluation of body-worn sensors even during longer walks. Experiments showed that both trunk and pocket positions achieved promising results in estimating gait parameters, with a mean absolute error below 50 ms.

Wearable Motion-Based Heart Rate at Rest: A Workplace Evaluation.

This paper studies the feasibility of using low-cost motion sensors to provide opportunistic heart rate assessments from ballistocardiographic signals during restful periods of daily life. Three wearable devices were used to capture peripheral motions at specific body locations (head, wrist, and trouser pocket) of 15 participants during five regular workdays each. Three methods were implemented to extract heart rate from motion data and their performance was compared to those obtained with an FDA-cleared device. With a total of 1358 h of naturalistic sensor data, our results show that providing accurate heart rate estimations from peripheral motion signals is possible during relatively "still" moments. In our real-life workplace study, the head-mounted device yielded the most frequent assessments (22.98% of the time under 5 beats per minute of error) followed by the smartphone in the pocket (5.02%) and the wrist-worn device (3.48%). Most importantly, accurate assessments were automatically detected by using a custom threshold based on the device jerk. Due to the pervasiveness and low cost of wearable motion sensors, this paper demonstrates the feasibility of providing opportunistic large-scale low-cost samples of resting heart rate.

Open-Label Study of the Stability of Sublingual Nitroglycerin Tablets in Simulated Real-Life Conditions

Contemporary practice favors refilling sublingual nitroglycerin (SL NTG) every 3 to 6 months. This recommendation is based on antiquated data that does not consider the reformulated tablet and the improved manufacturing process. Our objective was to investigate the stability of SL NTG over time using simulated real-life scenarios in comparison to controlled storage conditions. This was an open-label study of 100- and 25-count commercial SL NTG bottles stored in either controlled temperature and relative humidity conditions, or carried in a pocket or purse. SL NTG potency (chemical stability) was assessed using high performance liquid chromatography and physical stability was assessed by changes in tablet weights over time through the labeled expiration date. Both chemical and physical stability of SL NTG were affected by environmental and physical factors. High temperature storage resulted in the most rapid loss of potency. Tablets carried in a pant pocket lost potency faster than those carried in a purse. Potency was also dependent on headspace of the bottle. Tablets stored in the original bottle in a temperate environment could be expected to maintained potency for more than 2 years when carried in a purse, irrespective of package size. When carried in a pant pocket, potency of a 25-count bottle was maintained for 2 years, whereas potency of a 100-count bottle fell below acceptable limits at 12 months. In conclusion, since potency is dependent on temperature, headspace, and carrying practices, frequency of SL NTG refills should be based on individual patient behavior.

Quality of semen in young adult males who carry mobile phone in their front pocket of trousers or jeans

Quality of semen in young adult males who carry mobile phone in their front pocket of trousers or jeans

Gamma and Beta Absorbed Dose Conversion Coefficients in the Range from 10 keV to 10 MeV for Accidental Exposures From Point Sources Placed in Clothing in Proximity to the Body.

Retrospective dose assessment following acute radiation exposures during radiological incidents can be difficult and inaccurate due to the large uncertainties associated with dose estimation. However, rapid and accurate dose assessment is critical following an incident so that appropriate treatment can be provided to the patient as early as possible. Incident dose assessment relies heavily on biological dosimetry with corresponding large uncertainties for inhomogeneous exposures, resulting from the estimates of whole-body doses, while the assessment of absorbed doses to individual tissues might actually be more appropriate for acute radiation exposures. Incident exposure scenarios for orphan sources placed in a breast or back pants pocket were modeled using the International Commission on Radiological Protection computational reference male and female and the Monte Carlo N-particle code MCNP6 to compute absorbed dose conversion coefficients for organs of interest for monoenergetic photon and beta sources. The absorbed dose conversion coefficients are intended for use in conjunction with source information to rapidly estimate absorbed doses to organs of interest from radiological sources in one of the two pocket geometries. Absorbed dose conversion coefficients also have been calculated specifically for Co, Cs, and Ir. Those absorbed dose conversion coefficients were applied to data from a radiological incident in Yanango, Peru, for comparison with published dose assessments; the results agree within 20%. The conversion coefficients are expected to provide an accurate tool for assessing doses for the modeled geometries, provided uncertainties due to the exact source-body geometry and exposure time are considered.

When e-cigarettes go boom

On 23 November 2016, as he stood behind the counter of a wine store in New York City, Otis Gooding felt an explosion on his right thigh. The e-cigarette he’d been carrying in his pants pocket had suddenly erupted like a small fireworks display. As a security camera later showed, the 31-year-old Gooding struggled to pull the burning device out of his pants. Gooding would later need a skin graft and 51 staples, and he may never recover the full use of his hand.

Robust Pedestrian Dead Reckoning Based on MEMS-IMU for Smartphones.

This paper proposes a pedestrian dead reckoning (PDR) algorithm based on the strap-down inertial navigation system (SINS) using the gyros, accelerometers, and magnetometers on smartphones. In addition to using a gravity vector, magnetic field vector, and quasi-static attitude, this algorithm employs a gait model and motion constraint to provide pseudo-measurements (i.e., three-dimensional velocity and two-dimensional position increment) instead of using only pseudo-velocity measurement for a more robust PDR algorithm. Several walking tests show that the advanced algorithm can maintain good position estimation compare to the existing SINS-based PDR method in the four basic smartphone positions, i.e., handheld, calling near the ear, swaying in the hand, and in a pants pocket. In addition, we analyze the navigation performance difference between the advanced algorithm and the existing gait-model-based PDR algorithm from three aspects, i.e., heading estimation, position estimation, and step detection failure, in the four basic phone positions. Test results show that the proposed algorithm achieves better position estimation when a pedestrian holds a smartphone in a swaying hand and step detection is unsuccessful.

Validation of Freezing-of-Gait Monitoring Using Smartphone.

Freezing of gait (FOG) is a commonly observed motor symptom for patients with Parkinson's disease (PD). The symptoms of FOG include reduced step lengths or motor blocks, even with an evident intention of walking. FOG should be monitored carefully because it not only lowers the patient's quality of life, but also significantly increases the risk of injury. In previous studies, patients had to wear several sensors on the body and another computing device was needed to run the FOG detection algorithm. Moreover, the features used in the algorithm were based on low-level and hand-crafted features. In this study, we propose a FOG detection system based on a smartphone, which can be placed in the patient's daily wear, with a novel convolutional neural network (CNN). The walking data of 32 PD patients were collected from the accelerometer and gyroscope embedded in the smartphone, located in the trouser pocket. The motion signals measured by the sensors were converted into the frequency domain and stacked into a 2D image for the CNN input. A specialized CNN model for FOG detection was determined through a validation process. We compared our performances with the results acquired by the previously reported settings. The proposed architecture discriminated the freezing events from the normal activities with an average sensitivity of 93.8% and a specificity of 90.1%. Using our methodology, the precise and continuous monitoring of freezing events with unconstrained sensing can assist patients in managing their chronic disease in daily life effectively.

An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones †

Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the use of smartphone sensors. Use of smartphones for activity recognition poses challenges such as device independence and various usage behavior in terms of where the smartphone is kept. Only a few works address one or more of these challenges. Consequently, in this paper, we present a detailed activity recognition framework for identifying both static and dynamic activities addressing the above-mentioned challenges. The framework supports cases where (i) dataset contains data from accelerometer; and the (ii) dataset contains data from both accelerometer and gyroscope sensor of smartphones. The framework forms an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept (right pants pocket, shirt pockets or right hand). The framework is implemented and tested on real data set collected from 10 users with five different device configurations. It is observed that, with our proposed approach, 94% recognition accuracy can be achieved.

Thermal injuries from exploding electronic cigarettes

IntroductionThere are an estimated 2.75 million electronic cigarette (EC) users in the United States. ECs have become the most commonly used nicotine-containing product in young adults ages 18–24 years. Thermal, blast, and missile injuries from EC explosions has grown rapidly in recent years. Burn surgeons must remain up to date regarding management and treatment of burn injuries related to EC device ignition. MethodsAn IRB approved retrospective review of all patients admitted to the Massachusetts General Hospital Burn Center from January 2015 to April 2017 was performed. Fourteen patients with injuries associated with EC use were identified. Patient demographics, injury location, size and degree of burn, treatments required, length of stay (LOS), time to 95% closure, associated complications and injuries, and the circumstances that led to the injury were identified. ResultsThe mean age was 28.6±8.6 years with a range of 19–50 years (n=14). EC burns occurred in males 93% (13/14) of the time. The majority of EC explosions caused 2nd and 3rd degree burns (57%) within the same wound bed, followed by deep 2nd degree (29%), and superficial 2nd degree (14%). The average TBSA from EC burns was 4.7±2.4% with a range of 1–10%. The most common location of the device or battery at the time of the injury was a pant pocket 86% (12/14), followed by 7% hand (1/14) and 7% purse (1/14). Isolated lower extremity burns occurred in 43% (6/14) of patients, while lower extremity and hand burns occurred in 21% (3/14) of patients. Nine of 14 patients required an operating room encounter under general anesthesia. Eight of 14 patients required skin grafting for definitive wound closure. The mean hospital length of stay was 6.6±4.7 days with a range of 0–15 days. Time to 95% wound closure was 18.4±10.8 with a range of 8–40 days. ConclusionThermal and blast injuries associated with EC device failure tend to cause small TBSA burns that are deep 2nd and 3rd degree wounds. The most common location for EC device storage among males was the front pants pocket. EC device users should be made aware of the dangers associated with EC use and advised to carry EC devices away from their body in dedicated carrying cases without loose metallic items.

Smartphone App-Based Assessment of Gait During Normal and Dual-Task Walking: Demonstration of Validity and Reliability.

BackgroundWalking is a complex cognitive motor task that is commonly completed while performing another task such as talking or making decisions. Gait assessments performed under normal and “dual-task” walking conditions thus provide important insights into health. Such assessments, however, are limited primarily to laboratory-based settings.ObjectiveThe objective of our study was to create and test a smartphone-based assessment of normal and dual-task walking for use in nonlaboratory settings.MethodsWe created an iPhone app that used the phone’s motion sensors to record movements during walking under normal conditions and while performing a serial-subtraction dual task, with the phone placed in the user’s pants pocket. The app provided the user with multimedia instructions before and during the assessment. Acquired data were automatically uploaded to a cloud-based server for offline analyses. A total of 14 healthy adults completed 2 laboratory visits separated by 1 week. On each visit, they used the app to complete three 45-second trials each of normal and dual-task walking. Kinematic data were collected with the app and a gold-standard–instrumented GAITRite mat. Participants also used the app to complete normal and dual-task walking trials within their homes on 3 separate days. Within laboratory-based trials, GAITRite-derived heel strikes and toe-offs of the phone-side leg aligned with smartphone acceleration extrema, following filtering and rotation to the earth coordinate system. We derived stride times—a clinically meaningful metric of locomotor control—from GAITRite and app data, for all strides occurring over the GAITRite mat. We calculated stride times and the dual-task cost to the average stride time (ie, percentage change from normal to dual-task conditions) from both measurement devices. We calculated similar metrics from home-based app data. For these trials, periods of potential turning were identified via custom-developed algorithms and omitted from stride-time analyses.ResultsAcross all detected strides in the laboratory, stride times derived from the app and GAITRite mat were highly correlated (P<.001, r2=.98). These correlations were independent of walking condition and pocket tightness. App- and GAITRite-derived stride-time dual-task costs were also highly correlated (P<.001, r2=.95). The error of app-derived stride times (mean 16.9, SD 9.0 ms) was unaffected by the magnitude of stride time, walking condition, or pocket tightness. For both normal and dual-task trials, average stride times derived from app walking trials demonstrated excellent test-retest reliability within and between both laboratory and home-based assessments (intraclass correlation coefficient range .82-.94).ConclusionsThe iPhone app we created enabled valid and reliable assessment of stride timing—with the smartphone in the pocket—during both normal and dual-task walking and within both laboratory and nonlaboratory environments. Additional work is warranted to expand the functionality of this tool to older adults and other patient populations.

Recognition of a variety of activities considering smartphone positions

We present a high-accuracy recognition method for various activities using smartphone sensors based on device positions. Many researchers have attempted to estimate various activities, particularly using sensors such as the built-in accelerometer of a smartphone. Considerable research has been conducted under conditions such as placing a smartphone in a trouser pocket; however, few have focused on the changing context and influence of the smartphone position. Herein, we present a method for recognising seven types of activities considering three smartphone positions, and conducted two experiments to estimate each activity and identify the actual state under continuous movement at a university campus. The results indicate that the seven states can be classified with an average accuracy of 98.53% for three different smartphone positions. We also correctly identified these activities with 91.66% accuracy. Using our method, we can create practical services such as healthcare applications with a high degree of accuracy.