Wavelet analysis reveals differential lower limb muscle activity patterns long after anterior cruciate ligament reconstruction

Comparing the Effects of Static Stretching Alone and in Combination with Post-Activation Performance Enhancement on Squat Jump Performance at Different Knee Starting Angles

We aimed to investigate the impact of isolated static stretching (4 sets of 30 seconds) and its combined form with 10 repetitive drop jumps on lower limb performance during squat jumps at different knee joint starting angles (60°, 90°, and 120°). Thirteen participants completed three randomly ordered experimental visits, each including a standardized warm-up and squat jumps at three angles, apart from the intervention or control. Information was gathered through a three-dimensional movement tracking system, electromyography system, and force platform. The electromyography data underwent wavelet analysis to compute the energy values across the four wavelet frequency bands. The average power (Pavg), peak power (Ppeak), peak ground reaction force (GRFpeak), peak center of mass velocity (Vpeak), and force-velocity relationship at peak power (SFv) were extracted from the force and velocity-time data. The results revealed no significant influence of isolated static stretching, or its combined form with drop jumps, on the energy values across the frequency bands of the gastrocnemius, biceps femoris and rectus femoris, or the Pavg or Ppeak (P > 0.05). However, at 120°, static stretching reduced the GRFpeak (P = 0.001, d = 0.86) and SFv (P < 0.001, d = 1.12), and increased the Vpeak (P = 0.001, d = 0.5). The GRFpeak, Pavg, Ppeak, and SFv increased with an increase in the joint angle (P < 0.05), whereas the Vpeak decreased (P < 0.05). These findings suggest that static stretching does not diminish power output during squat jumps at the three angles; however, it alters GRFpeak, Vpeak, and the relative contributions of force and velocity to peak power at 120°, which can be eliminated by post-activation performance enhancement. Moreover, compared to 60° and 90°, 120° was more favorable for power and peak force output.

Long-Term Bilateral Neuromuscular Function and Knee Osteoarthritis after Anterior Cruciate Ligament Reconstruction

Neuromuscular function is thought to contribute to posttraumatic osteoarthritis (PTOA) risk in anterior cruciate ligament (ACL)-reconstructed (ACLR) patients, but sensitive and easy-to-use tools are needed to discern whether complex muscle activation strategies are beneficial or maladaptive. Using an electromyography (EMG) signal analysis technique coupled with a machine learning approach, we sought to: (1) identify whether ACLR muscle activity patterns differed from those of healthy controls, and (2) explore which combination of patient outcome measures (thigh muscle girth, knee laxity, hop distance, and activity level) predicted the extent of osteoarthritic changes via magnetic resonance imaging (MRI) in ACLR patients. Eleven ACLR patients 10-15 years post-surgery and 12 healthy controls performed a hop activity while lower limb muscle EMG was recorded bilaterally. Osteoarthritis was evaluated based on MRI. ACLR muscle activity patterns were bilaterally symmetrical and differed from those of healthy controls, suggesting the presence of a global adaptation strategy. Smaller ipsilateral thigh muscle girth was the strongest predictor of inferior MRI scores. The ability of our EMG analysis approach to detect meaningful neuromuscular differences that could ultimately be related to thigh muscle girth provides the foundation to further investigate a direct link between muscle activation patterns and PTOA risk.

Recent advancements in digital health management using multi-modal signal monitoring

Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term "Internet of Medical Things" (IoMT) refers to medical devices that have the ability to communicate data over a network without requiring human-to-human or human-to-computer interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery.