Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

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Sediment Bed Borehole Advection Method

Water ◽

10.3390/w12123380 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3380

Author(s):

Scott Augustine ◽

Jaehyun Cho ◽

Harald Klammler ◽

Kirk Hatfield ◽

Michael D. Annable

Keyword(s):

Time Series Data ◽

Low Cost ◽

Vertical Direction ◽

Series Data ◽

Time Analysis ◽

Sediment Bed ◽

Concentration Time Series ◽

Flux Measurements ◽

Darcy Flux ◽

Sediment Interface

This paper introduces and tests the Sediment Bed Borehole Advection Method (SBBAM), a low cost, point-measurement technique which utilizes a push-point probe to quantify the vertical direction and magnitude of Darcy flux at the surface water—groundwater sediment interface. The Darcy flux measurements are derived from the residence-time analysis of tracer arrival calculated from measured tracer concentration time-series data. The technique was evaluated in the laboratory using a sediment bed simulator tank at eight flow rates (1–90 cm/day). Triplicate test runs for each flow rate returned average errors between 4–20 percent; r2 = 0.9977.

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Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1510028112 ◽

2015 ◽

Vol 112 (49) ◽

pp. 15060-15065 ◽

Cited By ~ 179

Author(s):

Mark Z. Jacobson ◽

Mark A. Delucchi ◽

Mary A. Cameron ◽

Bethany A. Frew

Keyword(s):

Nuclear Power ◽

Large Scale ◽

Fossil Fuels ◽

Phase Change Materials ◽

Time Series Data ◽

Low Cost ◽

Series Data ◽

Integration Model ◽

Weather Model ◽

Scale Integration

This study addresses the greatest concern facing the large-scale integration of wind, water, and solar (WWS) into a power grid: the high cost of avoiding load loss caused by WWS variability and uncertainty. It uses a new grid integration model and finds low-cost, no-load-loss, nonunique solutions to this problem on electrification of all US energy sectors (electricity, transportation, heating/cooling, and industry) while accounting for wind and solar time series data from a 3D global weather model that simulates extreme events and competition among wind turbines for available kinetic energy. Solutions are obtained by prioritizing storage for heat (in soil and water); cold (in ice and water); and electricity (in phase-change materials, pumped hydro, hydropower, and hydrogen), and using demand response. No natural gas, biofuels, nuclear power, or stationary batteries are needed. The resulting 2050–2055 US electricity social cost for a full system is much less than for fossil fuels. These results hold for many conditions, suggesting that low-cost, reliable 100% WWS systems should work many places worldwide.

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Sample Entropy of Human Gait Center of Pressure Displacement: A Systematic Methodological Analysis

Entropy ◽

10.3390/e20080579 ◽

2018 ◽

Vol 20 (8) ◽

pp. 579 ◽

Cited By ~ 9

Author(s):

Samira Ahmadi ◽

Nariman Sepehri ◽

Christine Wu ◽

Tony Szturm

Keyword(s):

Time Series Data ◽

Inertial Sensors ◽

Center Of Pressure ◽

Sampling Rate ◽

Sample Entropy ◽

Human Gait ◽

Series Data ◽

Low Pass ◽

Data Points ◽

Parameter Values

Sample entropy (SampEn) has been used to quantify the regularity or predictability of human gait signals. There are studies on the appropriate use of this measure for inter-stride spatio-temporal gait variables. However, the sensitivity of this measure to preprocessing of the signal and to variant values of template size (m), tolerance size (r), and sampling rate has not been studied when applied to “whole” gait signals. Whole gait signals are the entire time series data obtained from force or inertial sensors. This study systematically investigates the sensitivity of SampEn of the center of pressure displacement in the mediolateral direction (ML COP-D) to variant parameter values and two pre-processing methods. These two methods are filtering the high-frequency components and resampling the signals to have the same average number of data points per stride. The discriminatory ability of SampEn is studied by comparing treadmill walk only (WO) to dual-task (DT) condition. The results suggest that SampEn maintains the directional difference between two walking conditions across variant parameter values, showing a significant increase from WO to DT condition, especially when signals are low-pass filtered. Moreover, when gait speed is different between test conditions, signals should be low-pass filtered and resampled to have the same average number of data points per stride.

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Estimation of Elasticities of Substitution for CES and VES Production Functions using Firm-level Data for Food-processing Industries in Pakistan

The Pakistan Development Review ◽

10.30541/v27i1pp.59-71 ◽

1988 ◽

Vol 27 (1) ◽

pp. 59-71 ◽

Cited By ~ 1

Author(s):

George E. Barrese ◽

Sohail J. Malik

Keyword(s):

Food Processing ◽

Agricultural Development ◽

Agricultural Sector ◽

Time Series Data ◽

Low Cost ◽

Series Data ◽

Firm Level ◽

Acre Yield ◽

Level Data ◽

The Government

This study, based on the time-series data covering the period from 1956 to 1986, estimates production function in the agricultural sector of Pakistan. The strategy for agricultural development in the country has been based on greater utilization of "high pay-off' low-cost technology. The government advanced loans through financial institutions to make it possible for the farmers to acquire this technology. Despite the infusion of seed-fertilizer technology, per acre yield of major crops like wheat, rice, cereal and sugar-cane in Pakistan is lower than in most LDCs in the region. Therefore, it is concluded that the use of present technology has reached a plateau and it is time to look for additional inputs for improvement in productivity.

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TSCache

Proceedings of the VLDB Endowment ◽

10.14778/3484224.3484225 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3253-3266

Author(s):

Jian Liu ◽

Kefei Wang ◽

Feng Chen

Keyword(s):

Time Series ◽

High Speed ◽

Time Series Data ◽

Low Cost ◽

Series Data ◽

Efficient System ◽

Indexing Structure ◽

System Solution ◽

Challenges And Opportunities ◽

Cache System

Time-series databases are becoming an indispensable component in today's data centers. In order to manage the rapidly growing time-series data, we need an effective and efficient system solution to handle the huge traffic of time-series data queries. A promising solution is to deploy a high-speed, large-capacity cache system to relieve the burden on the backend time-series databases and accelerate query processing. However, time-series data is drastically different from other traditional data workloads, bringing both challenges and opportunities. In this paper, we present a flash-based cache system design for time-series data, called TSCache . By exploiting the unique properties of time-series data, we have developed a set of optimization schemes, such as a slab-based data management, a two-layered data indexing structure, an adaptive time-aware caching policy, and a low-cost compaction process. We have implemented a prototype based on Twitter's Fatcache. Our experimental results show that TSCache can significantly improve client query performance, effectively increasing the bandwidth by a factor of up to 6.7 and reducing the latency by up to 84.2%.

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FSR and IMU sensors-based human gait phase detection and its correlation with EMG signal for different terrain walk

Sensor Review ◽

10.1108/sr-10-2020-0249 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Sachin Negi ◽

Shiru Sharma ◽

Neeraj Sharma

Keyword(s):

Gait Analysis ◽

Real Time ◽

Medial Gastrocnemius ◽

Human Gait ◽

Phase Detection ◽

Zero Crossing ◽

Content Type ◽

Open Environment ◽

Emg Signal ◽

Gait Phase

Purpose The purpose of this paper is to present gait analysis for five different terrains: level ground, ramp ascent, ramp descent, stair ascent and stair descent. Design/methodology/approach Gait analysis has been carried out using a combination of the following sensors: force-sensitive resistor (FSR) sensors fabricated in foot insole to sense foot pressure, a gyroscopic sensor to detect the angular velocity of the shank and MyoWare electromyographic muscle sensors to detect muscle’s activities. All these sensors were integrated around the Arduino nano controller board for signal acquisition and conditioning purposes. In the present scheme, the muscle activities were obtained from the tibialis anterior and medial gastrocnemius muscles using electromyography (EMG) electrodes, and the acquired EMG signals were correlated with the simultaneously attained signals from the FSR and gyroscope sensors. The nRF24L01+ transceivers were used to transfer the acquired data wirelessly to the computer for further analysis. For the acquisition of sensor data, a Python-based graphical user interface has been designed to analyze and display the processed data. In the present paper, the authors got motivated to design and develop a reliable real-time gait phase detection technique that can be used later in designing a control scheme for the powered ankle-foot prosthesis. Findings The effectiveness of the gait phase detection was obtained in an open environment. Both off-line and real-time gait events and gait phase detections were accomplished for the FSR and gyroscopic sensors. Both sensors showed their usefulness for detecting the gait events in real-time, i.e. within 10 ms. The heuristic rules and a zero-crossing based-algorithm for the shank angular rate correctly identified all the gait events for the locomotion in all five terrains. Practical implications This study leads to an understanding of human gait analysis for different types of terrains. A real-time standalone system has been designed and realized, which may find application in the design and development of ankle-foot prosthesis having real-time control feature for the above five terrains. Originality/value The noise-free data from three sensors were collected in the same time frame from both legs using a wireless sensor network between two transmitters and a single receiver. Unlike the data collection using a treadmill in a laboratory environment, this setup is useful for gait analysis in an open environment for different terrains.

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FIKWater: A Water Consumption Dataset from Three Restaurant Kitchens in Portugal

Data ◽

10.3390/data6030026 ◽

2021 ◽

Vol 6 (3) ◽

pp. 26

Author(s):

Lucas Pereira ◽

Vitor Aguiar ◽

Fábio Vasconcelos

Keyword(s):

Water Demand ◽

Time Series Data ◽

Cold Water ◽

Low Cost ◽

Sampling Frequency ◽

Research Community ◽

Series Data ◽

Flow Meters ◽

The Internet Of Things ◽

Size Data

With the advent of the Internet of Things (IoT) and low-cost sensing technologies, the availability of data has reached levels never imagined before by the research community. However, independently of their size, data are only as valuable as the ability to have access to them. This paper presents the FIKWater dataset, which contains time series data for hot and cold water demand collected from three restaurant kitchens in Portugal for consecutive periods between two and four weeks. The measurements were taken using ultrasonic flow meters, at a sampling frequency of 0.2 Hz. Additionally, some details of the monitored spaces are also provided.

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Foreign influence on Pakistan's exports and imports Evidence from Pakistan

Review of Economics and Development Studies ◽

10.26710/reads.v2i2.129 ◽

2016 ◽

Vol 2 (2) ◽

pp. 117-128

Author(s):

Irfan Hussain Khan ◽

Shumaila Hashim ◽

Muhammad Rizwan Yaseen

Keyword(s):

Trading Volume ◽

Time Series Data ◽

Low Cost ◽

Basic Research ◽

Developed Countries ◽

Theoretical Background ◽

Series Data ◽

Annual Data ◽

Study Results ◽

The Impact

The purpose of this study is to investigate the impact of the Pakistani currency phase action on exports and imports. Two time series data base year and quarterly basic research use. Starting from the 1970 annual data for about 40 years, beginning with the beginning of 2000 to 2012 quarterly data. Johnson estimates quarterly observations using common integration techniques. In the current study results show that Pakistan first began trading volume for the US and developed countries, the UK and Europe. As a combination of export and import time Pakistan has improved. Production and manufacture of semi-finished goods and primary product alternatives, while the import of consumer goods, capital goods and petroleum products expanded. Due to low-cost elasticity of the export and import activity of the exchange of theoretical background reaction support. On the other hand, if the value of the rupees fell against the dollar, the import costs rose more than the export bills. In support of this study, Pakistan should focus on a small number of countries to reduce trade and expand trade. Similarly, on the basis of the goods may add some other goods.

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Analysis of Privacy Preservation Techniques in IoT

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2019070104 ◽

2019 ◽

Vol 10 (3) ◽

pp. 27-33

Author(s):

Ravindra Sadashivrao Apare ◽

Satish Narayanrao Gujar

Keyword(s):

Artificial Intelligence ◽

Time Series ◽

Data Privacy ◽

Privacy Preservation ◽

Time Series Data ◽

Low Cost ◽

Series Data ◽

Automation System ◽

Sensing Technology ◽

Iot Devices

IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networking, big data, artificial intelligence, and sensing technology to distribute absolute systems for a service or product. The major challenges in IoT relies in security restrictions related with generating low cost devices, and the increasing number of devices that generates further opportunities for attacks. Hence, this article intends to develop a promising methodology associated with data privacy preservation for handling the IoT network. It is obvious that the IoT devices often generate time series data, where the range of respective time series data can be extremely large.

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