Showing posts with label EMG Signals. Show all posts

Wireless | Testing VO2 Max

►

Man running on treadmill to test his VO2 max

 Cardiovascular tests during a self-paced maximal exercise protocol (SPV) continually scored high ratings of VO2 max when compared to more traditional procedures. Jenkins et. al sought to understand the underlying causes of this increase in VO2 max by testing SPV versus the more regimented RAMP method. They sought to explore the results through extensive physiological measurement, as well as testing difference in older and younger age groups, while participants completed physical experiments.

The SPV protocol was completed on an air-braked cycle ergometer, which allowed participants to continually vary their Power Output (PO) throughout the test. An electro-magnetically braked cycle ergometer was used for the RAMP protocol, so that PO was fixed for each stage of the incremental RAMP protocol.

VO2 Max is essentially the maximum amount of oxygen utilized during a workout. Forty-four (44) male and female participants completed the experiment, half aged between 18- 30 and half between 50-75. The participants completed each test over a multi-day period. The tests were exhaustive, requiring subjects to cycle in place until they couldn’t any longer.

Jenkins et. al recorded various physiological signals including NIRS, breathing/expired gases, cardiac output/ stroke volume, blood lactate, and electromyography (EMG). BIOPAC’s BioNomadix research acquisition system wirelessly transmitted EMG data using two electrodes placed on participants’ right leg while they completed physical tasks.

Researchers were able find differences in the interaction effects of EMG between the two test protocols in the older group. The results complied with previous research, in that SPV allowed a higher VO2 max compared RAMP. Through monitoring physiological measurement, the study results suggested increased oxygen delivery as to an increase in oxygen-muscle extraction. The researchers found that there wasn’t a significance difference between the two testing protocols with the older population, though it’s unclear why. Overall, the experiment provides greater understanding of what causes differences in VO2 max between the two experimental procedures.

Wearable | Flow State in VR Video Games

►
wearable data about physiological response to VR
One of the foundational concepts making virtual reality (VR) video games of high interest is the game’s ability to transport the user to a flow state. The visual and auditory stimuli presented are highly immersive, leading the user to focus entirely on the game, often losing track of time. Flow states are characterized by this lack of time awareness, as participants are able to match their abilities with the demands of the game (i.e., the game is not too easy or too hard). But what qualities reflect a flow state as it is happening in the moment? Researchers from Shandong University tested five first-level physiological functions to determine their efficacy in reflecting a flow state, including EMG, EDA, EEG, respiratory rate, and cardiovascular activity. Thirty-six students participated in a VR game while having their physiological responses monitored. Prior to the experiment, the researchers placed BIOPAC’s wearable dual-signal BioNomadix transmitters with appropriate electrodes on the participants to wirelessly record Respiration and ECG data. Participants were then seated in the designated gaming chair for five minutes to record baseline responses. After this, they played the game for six minutes, followed by a questionnaire about flow experience. The results showed that the five physiological functions, as a whole, indicated flow state, though respiratory rate was most effective. The authors note that, as a physiologic arousal marker, respiratory rate best predicted flow state.

 Having physiologic indicators of a flow state not only assists future research, but also provides a method for real-time feedback on the efficacy of the game. The authors note that with better biometric data comes the opportunity to provide a better gaming experience, with real-time adjustments. If the physiologic responses and adjustments could be integrated with the gaming software, games would be far more realistic.

Wireless | Influence of Gender on Muscle Activity

►
Muscle mechanical energy expenditure shows the neuromotor strategies used by the nervous system to analyze human locomotion tasks and is directly related to its efficiency. Kaur, Shilpi, Bhatia, and Joshi investigated the impact of gender on the activity of agonist-antagonist muscles during maximum knee and ankle contraction in males and females. Twenty right leg dominant male and female adult volunteers were recruited in the study. Limb dominance was determined according to which leg the individual chooses and relies on to carry out the activities. Movements of knee and ankle used for the maximum contractions were knee flexion and extension, and ankle plantar flexion and dorsiflexion. EMG Signals were recorded wirelessly from the selected ipsilateral and contralateral muscles of both the dominant and non-dominant lower limbs of all subjects. Recordings used BIOPAC multi-channel Wireless EMG and the collected data was stored using AcqKnowledge software included with the data recording system. Results showed that there is no significant influence of gender on agonist-antagonist muscle energy expenditure during maximum knee contraction. For ankle contractions, gender has significant influence on energy expenditure during maximum ankle dorsiflexion. Researchers found that these results are helpful in understanding gender related differences in the energy expenditure of selected muscles during maximum knee and ankle contractions. The wireless BioNomadix modules used by the researchers permitted free movement for the knee and ankle movements required of the study. The Dynamometry-EMG BioNomadix Pair has matched transmitter and receiver module specifically designed to measure one or both signals. These units interface with the MP150 and data acquisition and AcqKnowledge software, allowing advanced analysis for multiple applications and supporting acquisition of a broad range of signals and measurements. Both channels have extremely high-resolution EMG and Dynamometry waveforms at the receiver’s output. The pair emulates a “wired” connection from the computer to subject, in terms of quality, but with all the benefits of a fully-wireless recording system.

Wireless Data | Sitting and Muscle Weakness

►

A growing health risk in modern times is the increased amount of time the average person spends sitting. Whether at work for 8 hours at a computer or on the couch all day watching a favorite show, sitting contributes to a sedentary lifestyle, which is a known risk factor for cardiovascular disease and diabetes. It has been found that even those who exercise regularly, yet spend a prolonged portion of their day seated, have increased risk of similar ailments. Though many health risks of sitting are known, there has been little research on its impact on the musculoskeletal system. Physical therapists have noted an inexplicably high rate of clinical weakness of the gluteus maximus muscle. Doctoral candidates in physical therapy at City University of New York recently published a capstone project on their hypothesis that the habit of prolonged sitting directly leads to weakening of the gluteus maximus and the hamstrings. In the experiment, subjects were asked, after a brief warm-up, to perform maximal voluntary isometric contraction (MVIC) for both muscle groups. In addition, two functional activities were performed by the subjects: a “sit-to-stand” exercise and a “forward step-up” exercise. The subjects were separated into two groups based on their sitting/standing habits throughout the day. Surface EMG signals were recorded from the subjects using a BioNomadix wireless EMG Transmitter and Receiver set, along with an MP150 data acquisition system. Using AcqKnowledge software, the researchers were able to process the raw EMG signals with automated data reduction routines and statistical analysis. Further analysis of the data found no statistically significant differences in gluteus strength between the two groups. However, the group still believes that there remains to be studied the muscular effects of prolonged sitting. Further studies may be benefitted by the use of the BioNomadix Logger for continuous, 24-hour logging of a range of physiological signals. BIOPAC offers BioNomadix wireless physiology systems and a number of other solutions for EMG and other signals and measurements.

EMG Analysis | Biomechanics

►
Biomechanics research has never been easier thanks to powerful new data acquisition and analysis tools. Perform real-time calculations and post-data acquisition analysis on a variety of biomechanical and physiological data.

Simultaneously acquire up to 16 channels of biomechanics and/or gait-specific data. An example setup could incorporate two channels of heel/toe strike timing, ten channels of EMG signals, and four channels of goniometry data — however combinations are virtually endless. Record sit-and-reach tests, range of motion evaluations, muscle balance assessments and more.

Real-time event markers allow researchers to log important events in the data and also include comments that can be written during or post acquisition.


After recording, choose an automated analysis package to interpret and score the biomechanics data. For example, automated EMG analysis allows for a variety of automated functions including deriving integrated EMG, root mean square (RMS) EMG, locating muscle activation, full frequency and power analysis, and much more.

- Copyright © Life Science Hardware and Software -Metrominimalist- Powered by Blogger - Designed by Johanes Djogan -