This study investigated whether using an inertial measurement unit (IMU) can identify different walking conditions, including level walking (LW), descent (DC) and ascent (AC) slope walking as well as downstairs (DS) and upstairs (US) walking. Thirty healthy participants performed walking under five conditions. The IMU was stabilised on the exterior of the left shoe. The data from IMU were used to establish a customised prediction model by cut point and a prediction model by using deep learning method. The accuracy of both prediction models was evaluated. The customised prediction model combining the angular velocity of dorsi–plantar flexion in the heel-strike (HS) and toe-off (TO) phases can distinctly determine real conditions during DC and AC slope, DS, and LW (accuracy: 86.7–96.7%) except for US walking (accuracy: 60.0%). The prediction model established by deep learning using the data of three-axis acceleration and three-axis gyroscopes can also distinctly identify DS, US, and LW with 90.2–90.7% accuracy and 84.8% and 82.4% accuracy for DC and AC slope walking, respectively. In conclusion, inertial measurement units can be used to identify walking patterns under different conditions such as slopes and stairs with customised prediction model and deep learning prediction model.

Passive leg press (PLP) training was developed based on the concepts of the stretch-shortening cycle (SSC) and the benefits of high muscle contraction velocity. Passive leg press training enables lower limb muscle groups to apply a maximum downward force against a platform moved up and down at high frequency by an electric motor. Thus, these muscle groups accomplished both concentric and eccentric isokinetic contractions in a passive, rapid, and repetitive manner. This study investigates the effects of 10 weeks of PLP training at high and low movement frequencies have on jumping performance, speed, and muscle power. The authors selected 30 college students who had not performed systematic resistance training in the previous 6 months, including traditional resistance training at a squat frequency of 0.5 Hz, PLP training at a low frequency of 0.5 Hz, and PLP training at a high frequency of 2.5 Hz, and randomly divided them into 3 groups (n = 10). The participants' vertical jump, drop jump, 30-m sprint performance, explosive force, and SSC efficiency were tested under the same experimental procedures at pre- and post-training. Results reveal that high-frequency PLP training significantly increased participants' vertical jump, drop jump, 30-m sprint performance, instantaneous force, peak power, and SSC efficiency (p < 0.05). Additionally, their change rate abilities were substantially superior to those of the traditional resistance training (p < 0.05). The low-frequency PLP training significantly increased participants' vertical jump, 30-m sprint performance, instantaneous force, and peak power (p < 0.05). However, traditional resistance training only increased participants' 30-m sprint performance and peak power (p < 0.05). The findings suggest that jump performance, speed, and muscle power significantly improved after 10 weeks of PLP training at high movement frequency. A PLP training machine powered by an electrical motor enables muscles of the lower extremities to contract faster compared with voluntary contraction. Therefore, muscle training with high contraction velocity is one of the main methods of increasing muscle power. Passive leg press training is a unique method for enhancing jump performance, speed, and muscle power.

-mm thickness. The magnetic resonance digital images were analyzed using medical imaging software. This study separated the human body into 14 segments, including the head and neck, trunk, upper arm, forearm, hand, thigh, shank, and foot to calculate the percentage of mass of total body mass (%M), center of mass location (%CM), and moments of inertia (I) for each segment. Results The study results showed %M and %CM of the head and neck (8.21 and 52.69), trunk (42.28 and 41.96), upper arm (3.25 and 47.16), forearm (1.36 and 40.98), hand (0.54 and 32.60), thigh (13.50 and 48.56), shanks (4.63 and 41.68), and foot (1.47 and 47.51). Compared with other cadaver-based and in vivo studies, our findings indicate a lower %M and %CM at the hand, forearm, and trunk but higher %M and %CM at the head. The differences may be due to the race, lifestyle, age, and level of participants. The discrepancies in segment boundaries and coordinate system may also contribute to these differences. Most previous studies have either used cadavers from older Caucasian men or drawn conclusions from a limited sample size. Conclusions This study establishes a new model to provide thorough information of Chinese body segment parameters for enhanced ergonomic design and more accurate human movement studies....