Lower limb exoskeletons and lower limb prostheses have the potential to reduce gait limitations during stair ambulation. To develop robotic assistance devices, the biomechanics of stair ambulation and the required transitions to level walking have to be understood. This study aimed to identify the timing of these transitions, to determine if transition phases exist and how long they last, and to investigate if there exists a joint-related order and timing for the start and end of the transitions. Therefore, this study analyzed the kinematics and kinetics of both transitions between level walking and stair ascent, and between level walking and stair descent (12 subjects, 25.4 yrs, 74.6 kg). We found that transitions primarily start within the stance phase and end within the swing phase. Transition phases exist for each limb, all joints (hip, knee, ankle), and types of transitions. They have a mean duration of half of one stride and they do not last longer than one stride. The duration of the transition phase for all joints of a single limb in aggregate is less than 35% of one stride in all but one case. The distal joints initialize stair ascent, while the proximal joints primarily initialize the stair descent transitions. In general, the distal joints complete the transitions first. We believe that energy- and balance-related processes are responsible for the joint-specific transition timing. Regarding the existence of a transition phase for all joints and transitions, we believe that lower limb exoskeleton or prosthetic control concepts should account for these transitions in order to improve the smoothness of the transition and to thus increase the user comfort, safety, and user experience. Our gait data and the identified transition timings can provide a reference for the design and the performance of stair ambulation- related control concepts.
Human-in-the-loop (HITL) optimization with metabolic cost feedback has been proposed to reduce walking effort with wearable robotics. This study investigates if lower limb surface electromyography (EMG) could be an alternative feedback variable to overcome time-intensive metabolic cost based exploration. For application, it should be possible to distinguish conditions with different walking efforts based on the EMG. To obtain such EMG data, a laboratory experiment was designed to elicit changes in the effort by loading and unloading pairs of weights (in total 2, 4, and 8 kg) in three randomized weight sessions for 13 subjects during treadmill walking. EMG of seven lower limb muscles was recorded for both limbs. Mean absolute values of each stride prior to and following weight loading and unloading were used to determine the detection rate (100% if every loading and unloading is detected accordingly) for changing between loaded and unloaded conditions. We assessed the use of multiple consecutive strides and the combination of muscles to improve the detection rate and estimated the related acquisition times of diminishing returns. To conclude on possible limitations of EMG for HITL optimization, EMG drift was evaluated during the Warmup and the experiment. Detection rates highly increased for the combination of multiple consecutive strides and the combination of multiple muscles. EMG drift was largest during Warmup and at the beginning of each weight session. The results suggest using EMG feedback of multiple involved muscles and from at least 10 consecutive strides (5.5 s) to benefit from the increases in detection rate in HITL optimization. In combination with up to 20 excluded acclimatization strides, after changing the assistance condition, we advise exploring about 16.5 s of walking to obtain reliable EMG-based feedback. To minimize the negative impact of EMG drift on the detection rate, at least 6 min of Warmup should be performed and breaks during the optimization should be avoided. Future studies should investigate additional feedback variables based on EMG, methods to reduce their variability and drift, and should apply the outcomes in HITL optimization with lower limb wearable robots.
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