Determining motions with an IMU during level walking and slope and stair walking.

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.

Walking beyond preferred transition speed increases muscle activations with a shift from inverted pendulum to spring mass model in lower extremity.

BACKGROUND: The triggers for the transition of gait from walking to running during increasing speed locomotion have been attributed to an energy conservation strategy or a relief of excessive muscle activation. Walking beyond the preferred transition speed (PTS) has been proposed as an exercise protocol for boosting energy consumption. However, the biomechanical factors involved while this protocol is used have not been investigated. Thus, this study investigated the difference between walking and running below, during, and beyond the PTS from a biomechanical perspective. METHODS: Sixteen healthy male participants were recruited. After determination of their PTS, five speeds of walking and running were defined. Kinematic data, including center-of-mass (COM) displacement, COM acceleration, and electromyography (EMG) data of rectus femoris (RF), biceps femoris, gastrocnemius (GAS), and tibialis anterior were collected at the five speeds for both walking and running. RESULT: The vertical COM displacement and acceleration in running were significantly larger than those in walking at all five speeds (p<0.05). EMG signals of the two antigravity muscles, RF and GAS, demonstrated a significant higher activation in walking than that in running at the speed beyond PTS (p<0.05). CONCLUSION: The larger energy consumption in walking than that in running beyond the PTS may be attributed to the high activation of lower-extremity muscles. The smaller vertical COM displacements and accelerations exhibited when participants walked beyond the PTS rather than ran did not indicate adverse effects of using walking beyond the PTS as an exercise prescription for boosting energy consumption.

Does functional fitness decline in accordance with our expectation? - a pilot study in healthy female.

BACKGROUND: Aging may cause various functional abilities gradually deteriorate. With changes in social forms, the trend of functional fitness decline will change accordingly. Therefore, this study endeavored to identify the trends in functional fitness decline by comparing the differences in the functional fitness of females in various age groups. METHODS: Thirty six healthy females were divided into 3 age groups: young healthy females (20 to 30 y); middle-age (45 to 55 y); and older (65 to 75 y). Functional fitness test battery included flexibility, muscle strength/endurance, aerobic endurance, balance and agility. RESULTS: The performance in the elderly group was significantly worse (P < .05) in all the tests, whereas the muscle strength and endurance, as well as aerobic endurance for the middle-age group showed significantly lower than young groups (P < .05). CONCLUSIONS: The reduction in lower extremity muscle strength occurs in the middle-age group. We recommend that middle-age women be conscious of the reduction in their lower extremity muscle strength and conduct advanced preparations for future aging.

Assessment of walking, running, and jumping movement features by using the inertial measurement unit.

PURPOSE: To observe various modes of lower limb locomotion, an inertial measurement unit (IMU) was used. Digital signals were used to identify signal characteristics that help to distinguish among locomotion modes and intensity levels. METHODS: A wireless IMU was installed on the outside of shoes and three forms of locomotion (walking, running, and jumping) were assessed at two intensity levels (low and high) to observe the acceleration, foot angular velocity variations, and characteristics of the curve variations in the anteroposterior, mediolateral, and superior-inferior directions. RESULTS: Most interactions between intensity and locomotion were statistically significant, except for the acceleration in the anteroposterior direction and on the horizontal plane. In addition, as the intensity increased, the values of all the parameters increased. Thus, both the acceleration values and range of angular velocity variation can be used to distinguish the intensity levels. Moreover, the results indicated that the angular velocity in the frontal axis, which is the sequence of the plantar/dorsiflexion movements, can also be used to identify different locomotion. CONCLUSIONS: Uniaxial acceleration or the range of angular velocity variation could be used to identify locomotion intensities, whereas the characteristics of the uniaxial angular velocity curve could be used to identify the locomotion modes.