scholarly journals Secure Microprocessor-Controlled Prosthetic Leg for Elderly Amputees: Preliminary Results

2011 ◽  
Vol 8 (3-4) ◽  
pp. 385-398 ◽  
Author(s):  
S. Krut ◽  
Ch. Azevedo Coste ◽  
P. Chabloz
Keyword(s):  
Force Sensor ◽  
Detection Algorithm ◽  
Stair Climbing ◽  
Sensor Data ◽  
Reference Pattern ◽  
Prosthetic Joint ◽  
Preliminary Validation ◽  
Standing Up ◽  
Leg Design ◽  
Capabilities Transfer

We introduce a new prosthetic leg design, adapted to elderly trans-femoral amputees. Technical progress in prosthesis design mainly concerns active individuals. An important number of elderly amputees are not very mobile, tire easily, present reduced muscle strength, and have difficulties managing their balance. Therefore, the needs and characteristics of this specific population are very different from those of younger ones and the prosthetic solutions are not adapted. Our artificial knee has been designed to fulfill the specific requirements of this population in terms of capabilities, transfer assistance, security, intuitiveness, simplicity of use, and types of physical activity to be performed. We particularly focused our efforts on ensuring safe and secure stand-to-sit transfers. We developed an approach to control the different states of the prosthetic joint (blocked, free, resistant), associated with different physical activities. Amputee posture and motion are observed through a single multi-axis force sensor embedded in the prosthesis. The patient behaves naturally, while the controller analyses his movements in order to detect his intention to sit down. The detection algorithm is based on a reference pattern, calibrated individually, to which the sensor data are compared, and submitted to a set of tests allowing the discrimination of the intention to sit down from other activities. Preliminary validation of the system has been performed in order to verify the applicability of the prosthesis to different tasks: walking, standing, sitting down, standing up, picking up an object from a chair, slope and stair climbing.

A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

2020 ◽  
Author(s):  
Meenakshi Narayan ◽  
Ann Majewicz Fey
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Real Time ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Force Sensor ◽  
Sensor Data ◽  
Series Data ◽  
Model Free ◽  
Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

scholarly journals Development of a Reasonable Force Sensor for a Standing-up and Sitting Motion Support System

2004 ◽  
Vol 2004 (0) ◽  
pp. 53 ◽  
Author(s):  
T Sugihara ◽  
K Kawabata ◽  
H Kaetsu ◽  
H Asama ◽  
K Kosuge ◽  
...  
Keyword(s):  
Support System ◽  
Force Sensor ◽  
Standing Up

Output force of giant magnetostrictive actuator

2019 ◽  
Vol 33 (25) ◽  
pp. 1950301
Author(s):  
Yan Wu ◽  
Xuhui Liu ◽  
Tiantian Guo ◽  
Ye Qiu
Keyword(s):  
Force Sensor ◽  
Test System ◽  
Sensor Data ◽  
Magnetostrictive Materials ◽  
Output Force ◽  
Wide Range ◽  
Giant Magnetostrictive Materials ◽  
Data Acquisition Card ◽  
Potential Applications ◽  
Magnetostrictive Actuator

A new type of intelligent micro displacement materials, giant magnetostrictive materials (GMM), have a wide range of potential applications in the fields of micro vibrations. In this paper, a novel type of giant magnetostrictive actuator (GMA), mainly made by the giant magnetostrictive materials, is designed, and its inner structure and working principle are also discussed. To investigate the output force of giant magnetostrictive actuator, a test system, including the force sensor, data acquisition card and power supply equipment are established. The experimental results show, when the excited current increased from 0.5 A to 2 A gradually, the output force of the giant magnetostrictive actuator also gradually increased, in the condition of the pre-compress force from 100 N to 400 N, the output force of the giant magnetostrictive actuator will also increase with the increasing of the pre-compress force.

scholarly journals Fall detection system based on BiLSTM neural network

2019 ◽  
Vol 7 (5) ◽  
pp. 01-12
Author(s):  
Biao YE ◽  
Lasheng Yu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Detection System ◽  
Input Sequence ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Training Data ◽  
Sensor Data ◽  
Data Set ◽  
Sequence Modeling

The purpose of this article is to analyze the characteristics of human fall behavior to design a fall detection system. The existing fall detection algorithms have problems such as poor adaptability, single function and difficulty in processing large data and strong randomness. Therefore, a long-term and short-term memory recurrent neural network is used to improve the effect of falling behavior detection by exploring the internal correlation between sensor data. Firstly, the serialization representation method of sensor data, training data and detection input data is designed. The BiLSTM network has the characteristics of strong ability to sequence modeling and it is used to reduce the dimension of the data required by the fall detection model. then, the BiLSTM training algorithm for fall detection and the BiLSTM-based fall detection algorithm convert the fall detection into the classification problem of the input sequence; finally, the BiLSTM-based fall detection system was implemented on the TensorFlow platform. The detection and analysis of system were carried out using a bionic experiment data set which mimics a fall. The experimental results verify that the system can effectively improve the accuracy of fall detection to 90.47%. At the same time, it can effectively detect the behavior of Near-falling, and help to take corresponding protective measures.

scholarly journals Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 448 ◽  
Author(s):  
Reza Rawassizadeh ◽  
Chelsea Dobbins ◽  
Mohammad Akbari ◽  
Michael Pazzani
Keyword(s):  
Event Detection ◽  
Clustering Algorithm ◽  
Search Space ◽  
Detection Algorithm ◽  
Sensor Data ◽  
Data Types ◽  
Contextual Data ◽  
User Behaviors ◽  
Clustering Approach ◽  
Spatio Temporal

Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering approach can be used as an annotation on raw sensor data. It improves information retrieval by reducing the search space and is based on searching only the related clusters. To further improve behavior quantification, the third algorithm identifies contrasting events withina cluster content. Two large real-world smartphone datasets have been used to evaluate our algorithms and demonstrate the utility and resource efficiency of our approach to search.

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