Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking

Abstract

Lower limb exoskeletons require the correct support magnitude and timing to achieve user assistance. This study evaluated whether the sign of the angular velocity of lower limb segments can be used to determine the timing of the stance and the swing phase during walking. We assumed that stance phase is characterized by a positive, swing phase by a negative angular velocity. Thus, the transitions can be used to also identify heel-strike and toe-off. Thirteen subjects without gait impairments walked on a treadmill at speeds between 0.5 and 2.1 m/s on level ground and inclinations between −10 and +10°. Kinematic and kinetic data was measured simultaneously from an optical motion capture system, force plates, and five inertial measurement units (IMUs). These recordings were used to compute the angular velocities of four lower limb segments: two biological (thigh, shank) and two virtual that were geometrical projections of the biological segments (virtual leg, virtual extended leg). We analyzed the reliability (two sign changes of the angular velocity per stride) and the accuracy (offset in timing between sign change and ground reaction force based timing) of the virtual and biological segments for detecting the gait phases stance and swing. The motion capture data revealed that virtual limb segments seem superior to the biological limb segments in the reliability of stance and swing detection. However, increased signal noise when using the IMUs required additional rule sets for reliable stance and swing detection. With IMUs, the biological shank segment had the least variability in accuracy. The IMU-based heel-strike events of the shank and both virtual segment were slightly early (3.3–4.8% of the gait cycle) compared to the ground reaction force-based timing. Toe-off event timing showed more variability (9.0% too early to 7.3% too late) between the segments and changed with walking speed. The results show that the detection of the heel-strike, and thus stance phase, based on IMU angular velocity is possible for different segments when additional rule sets are included. Further work is required to improve the timing accuracy for the toe-off detection (swing).

Lower limb joint biomechanics-based identification of gait transitions in between level walking and stair ambulation

“…The end of the stride could not be determined by vertical GRF in the same setup (a, b, Fig 1 ) for some of the used strides. For the strides A10, D10, D11, and A11, the end of the stride was therefore determined based on the shank angular velocity zero-crossing, including the removal of the time offset (3% of the gait cycle) based on the findings of [ 26 , 27 ]. As this approach did not work well for some of the subjects for the stair ambulation strides, another approach was required to determine the end of the stride for D3, D4, A6, and A7.…”

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

“…These computation methods are categorized into two main domains. Firstly, the domain based on the threshold method [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], time-frequency analysis [ 18 , 19 , 20 , 21 ], and peak heuristic algorithms [ 16 , 19 , 22 , 23 , 24 , 25 ], which are also variations of the threshold method. Secondly, Machine Learning (ML) approaches are now among the most popular techniques to detect phases and events with various models such as Hidden Markov Models (HMM) [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ], or several of the latest studies published based on the Artificial Neural Network technique (ANN) [ 35 , 36 , 37 , 38 ], Deep Learning Neural Network (DLNN) [ 39 , 40 , 41 , 42 , 43 ], a Convolutional Neural Network (CNN) [ 44 , 45 , 46 ], or [ 28 ] proposed a hybrid method that combined HMM and Fully connected Neural Networks (FNN).…”

“…The models are adopted depending on the type of sensors that are installed for recording the signals of the gait. Nowadays, wearable sensors are widely used for gait phase recognition systems: Wearable force-based measurements [ 9 , 21 , 26 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ], Electromyographic (EMG) sensors [ 55 , 56 ], Inertial Measurement Units (IMUs) [ 9 , 19 , 29 , 41 , 57 , 58 ], and joint angular sensors [ 24 , 59 , 60 , 61 , 62 ] are used specifically for the detection of the gait. The studies showed that the methods that used force-based measurements such as Force Sensing Resistors (FSRs), footswitches, and foot pressure insoles yield the highest precision for detection [ 7 ].…”

Control Reference Parameter for Stance Assistance Using a Passive Controlled Ankle Foot Orthosis—A Preliminary Study

“…The implementation of the control reference parameter using the MR brake is also not difficult, because the feedback signal can be measured directly by using a rotary encoder. Ankle velocity can also be used to classify the gait phases [36]. This means that only rotary encoder is necessary as the sensor for both classification and control function in future implementation.…”