scholarly journals Monitoring Lower Back Activity in Daily Life Using Small Unintrusive Sensors and Wearable Electronics in the Context of Rheumatic and Musculoskeletal Diseases

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6362
Author(s):  
Mathieu Baijot ◽  
Robert Puers ◽  
Michael Kraft
Keyword(s):  
Muscle Activity ◽  
Musculoskeletal Diseases ◽  
Daily Life ◽  
Sensor System ◽  
Wearable Electronics ◽  
Lower Back ◽  
Wearable Systems ◽  
Long Term Measurement ◽  
Electromyography Signals

Due to a sedentary lifestyle, the amount of people suffering from musculoskeletal back diseases has increased over the last few decades. To monitor and cure these disabilities, sensors able to monitor the patient for long-term measurement during daily life and able to provide real-time feedback are required. There are only a few wearable systems that are capable to acquire muscle activity (sEMG) and posture at the same time. Moreover, previously reported systems do not target back sensor and typically comprise bulky uncomfortable solutions. In this paper, we present a new wearable sensor network that is designed to measure muscle activity and posture specialized for back measurement. Special care was taken to propose a discrete and comfortable solution. The prototype only measures 3.1 mm in thickness on the spine, making this sensor system the thinnest and lightest one in the literature to our best knowledge. After testing, it was shown that the sensor system is able to acquire two surface electromyography signals concurrently, to gather acceleration and rotation speed from the patient’s lower back, and to transmit data to a computer or a smartphone via serial communication or Bluetooth low energy for a few hours for later processing and analysis.

scholarly journals P-18　Development of a lower-back muscle activity sensor system for educational support for care workers

2017 ◽  
Vol 53 (Supplement2) ◽  
pp. S736-S737
Author(s):  
Tsuyoshi Yi YOTO ◽  
Takeshi KOMATSU ◽  
Shigeo MURAMATSU ◽  
Akira NAKAI ◽  
Yoshihiro SHIMOMURA ◽  
...  
Keyword(s):  
Muscle Activity ◽  
Sensor System ◽  
Back Muscle ◽  
Educational Support ◽  
Lower Back ◽  
Care Workers

scholarly journals Contactless Gait Assessment in Home-like Environments

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6205
Author(s):  
Angela Botros ◽  
Nathan Gyger ◽  
Narayan Schütz ◽  
Michael Single ◽  
Tobias Nef ◽  
...  
Keyword(s):  
Cycle Time ◽  
Sensor System ◽  
Sensor Technology ◽  
Coefficient Of Determination ◽  
Measurement Unit ◽  
Gait Assessment ◽  
Gait Parameters ◽  
Long Term Measurement ◽  
Very High

Gait analysis is an important part of assessments for a variety of health conditions, specifically neurodegenerative diseases. Currently, most methods for gait assessment are based on manual scoring of certain tasks or restrictive technologies. We present an unobtrusive sensor system based on light detection and ranging sensor technology for use in home-like environments. In our evaluation, we compared six different gait parameters, based on recordings from 25 different people performing eight different walks each, resulting in 200 unique measurements. We compared the proposed sensor system against two state-of-the art technologies, a pressure mat and a set of inertial measurement unit sensors. In addition to test usability and long-term measurement, multi-hour recordings were conducted. Our evaluation showed very high correlation (r>0.95) with the gold standards across all assessed gait parameters except for cycle time (r=0.91). Similarly, the coefficient of determination was high (R2>0.9) for all gait parameters except cycle time. The highest correlation was achieved for stride length and velocity (r≥0.98,R2≥0.95). Furthermore, the multi-hour recordings did not show the systematic drift of measurements over time. Overall, the unobtrusive gait measurement system allows for contactless, highly accurate long- and short-term assessments of gait in home-like environments.

Continuous automatic measurement of rhythms in fungal respiration using a gas chromatograph

1973 ◽  
Vol 51 (4) ◽  
pp. 701-710 ◽  
Author(s):  
Roger S. Smith
Keyword(s):  
Carbon Dioxide ◽  
Agar Medium ◽  
Quantitative Measure ◽  
Automatic Measurement ◽  
Carbon Dioxide Production ◽  
Chromatographic Technique ◽  
Gas Chromatograph ◽  
Growth Chambers ◽  
Long Term Measurement

The long-term measurement of aerobic fungal respiration, both on an agar medium and on wood blocks, was possible using a gas-chromatographic technique for the detection of the carbon dioxide. This method was fully automated to analyze gas samples sequentially from eight or more growth chambers, after variable but determined time periods. It provided a precise quantitative measure of the respired carbon dioxide, presented both in the form of punched computer tape and normal printed teleprinter output. This apparatus worked continuously for several years without serious breakdown.The fungi Lentinus lepideus, Lenzites trabea, Poria monticola, and several strains of Coniophora puteana all showed a rhythm in their respiration which was not controlled by temperature or light. The magnitude and frequency of the rhythmical peaks in carbon dioxide production varied between fungi and, although there was considerable variation between different isolates of the same species, the separation of these species of fungi based on their different patterns of respiration was possible.

A long-term stable paper-based glucose sensor using a glucose oxidase-loaded, Mn2BPMP-conjugated nanocarrier with a smartphone readout

Nanoscale ◽  
2021 ◽  
Author(s):  
Soyeon Yoo ◽  
Kiyoon Min ◽  
Giyoong Tae ◽  
Min Su Han
Keyword(s):  
Glucose Oxidase ◽  
Glucose Sensor ◽  
Sensor System ◽  
Term Stability ◽  
Long Term Stability

A paper-type sensor system was devised using an enzyme-loaded, artificial peroxidase-conjugated nanocarrier to maintain long-term stability with smartphone readout.

scholarly journals Muscle Activity and Inactivity Periods during Normal Daily Life

PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e52228 ◽  
Author(s):  
Olli Tikkanen ◽  
Piia Haakana ◽  
Arto J. Pesola ◽  
Keijo Häkkinen ◽  
Timo Rantalainen ◽  
...  
Keyword(s):  
Muscle Activity ◽  
Daily Life
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