Huang, Z-H, Ma, CZ-H, Wang, L-K, Wang, X-Y, Fu, S-N, and Zheng, Y-P. Real-time visual biofeedback via wearable ultrasound imaging can enhance the muscle contraction training outcome of young adults. J Strength Cond Res 36(4): 941–947, 2022—Real-time ultrasound imaging (RUSI) can serve as visual biofeedback to train deep muscle contraction in clinical rehabilitative settings. However, its effectiveness in resistance training in sports/fitness fields remains unexplored. This article introduced a newly developed wearable RUSI system that provided visual biofeedback of muscle thickening and movement and reported its effectiveness in improving the training outcomes of muscle thickness change (%) during dynamic contraction. Twenty-five healthy young men participated and performed pec fly exercise both with and without RUSI biofeedback. Statistical analysis was conducted to examine the reliability of the measurements and the immediate effects of (a) RUSI biofeedback of muscle contraction and (b) training intensity (50 vs. 80% of 1-repetition maximum [1RM]) on the pectoralis major (PMaj) thickness change measured by ultrasound images. In addition to significantly high inter-contraction reliability (ICC3,1 > 0.97), we observed significantly increased PMaj thickness change for both training intensities upon receiving biofeedback in subjects, compared with without biofeedback (p < 0.001). We also observed significantly larger PMaj thickness change at 80% of 1RM compared with 50% of 1RM (p = 0.023). The provision of visual biofeedback using RUSI significantly enlarged the magnitude of PMaj thickness change during pec fly exercises, potentially indicating that RUSI biofeedback could improve the ability of targeted muscle contraction of PMaj in healthy young adults. To our knowledge, this study has pioneered in applying RUSI as a form of biofeedback during weight training and observed positive effectiveness. Future iterations of the technique will benefit more subject groups, such as athletes and patients with neuromuscular disorders.

As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications, especially in revealing the functional and molecular information to improve diagnostic accuracy. However, stemming from weak amplitude and unavoidable random noise, caused by limited laser power and severe attenuation in deep tissue imaging, PA signals are usually of low signal‐to‐noise ratio, and reconstructed PA images are of low quality. Despite that conventional Kalman filter (KF) can remove Gaussian noise in time domain, it lacks adaptability in real‐time estimation due to its fixed model. Moreover, KF‐based denoising algorithm has not been applied in PAI before. In this paper, we propose an adaptive modified KF (MKF) targeted at PAI denoising by tuning system noise matrix Q and measurement noise matrix R in the conventional KF model. Additionally, in order to compensate the signal skewing caused by MKF, we cascade the backward part of Rauch–Tung–Striebel smoother, which utilizes the newly determined Q. Finally, as a supplement, we add a commonly used differential filter to remove in‐band reflection artifacts. Experimental results using phantom and ex vivo colorectal tissue are provided to prove validity of the algorithm.

Background Falls in senior people have high incidence& lead to severe injuries [1]. Application of smart wearable systems (with sensors to monitor user’s balance and corresponding instant reminder to let tusers adjust posture/motion) can effectively improve static standing balance [2], reduce reaction time and body sway in response to balance perturbation [3], improve walking pattern [4], and reduce the risk of falls [5, 6]. However, previous systems have not considered the daily monitor of user’s balance and falling risks, and the personalized reminder. Artificial intelligence (AI) and big data analytics have been widely used to monitor the daily physical activity [7], while few studies have utilized them to improve balance/gait and prevent falls. Methods This study has optimized previous devices by integrating AI technology and developed a new smart insole system. The system consisted of insoles with embedded sensors that can capture the foot motion and plantar pressure, smart watch that connected with insoles wirelessly and then transmitted the foot motion and force data to Cloud server via Wi-Fi, central Cloud server for big data transmission and storage, workstation for big data analytics and machine learning, and user interface for data visualization (e.g. smartphone, tablet, and/or laptop). Results & Discussion The system transmission rate was up to 30 Hz. The collected big data contained all sensor signals captured before and after delivering reminder, and from day-to-day monitoring of users. The customized reminder varied in the type, frequency, magnitude, and amount/dosage. This AI smart insole system enabled the monitor of daily balance and falling risks and the provision of timely-updated and customized reminder to users, which could potentially reduce the risk of falls and slips. It can also act as a balance-training device. References 1. Rubenstein.Age ageing, 2006. 35(suppl2):p.ii37-ii41. 2. Ma.Sensors, 2015. 15(12):p.31709-31722. 3. Ma&Lee.Human Movement Science, 2017. 55:p.54-60. 4. Ma.Topics in stroke rehabilitation, 2018. 25(1):p.20-27. 5. Wan.Archives of physical medicine& rehabilitation, 2016. 97(7):p.1210-1213. 6. Ma.Sensors, 2016. 16(4):p.434. 7. Badawi.Future Generation Computer Systems, 2017. 66:p.59-70.

Wearable devices play an important role in our daily life. We can use it in the sports area and the medical area. This review uses many articles to introduce how to use different sensors to analyze human kinetic characteristics. This review is divided into three parts. In the first sections, we will introduce the working principle and its calculation method of the different sensors such as IMUs sensors, pressure transducers, etc. The second part will show you some research about using wearable sensors to calculate the joint data and link the data with human movement. These researches can help us stay far away from joint diseases. In conclusion, we give some suggestions about the improvement of wearable devices.