Assessing post-anterior cruciate ligament reconstruction ambulation using wireless wearable integrated sensors

Abstract

A hardware/software co-design for assessing post-Anterior Cruciate Ligament (ACL) reconstruction ambulation is presented. The knee kinematics and neuromuscular data during walking (2-6 km h(-1)) have been acquired using wireless wearable motion and electromyography (EMG) sensors, respectively. These signals were integrated by superimposition and mixed signals processing techniques in order to provide visual analyses of bio-signals and identification of the recovery progress of subjects. Monitoring overlapped signals simultaneously helps in detecting variability and correlation of knee joint dynamics and muscles activities for an individual subject as well as for a group. The recovery stages of subjects have been identified based on combined features (knee flexion/extension and EMG signals) using an adaptive neuro-fuzzy inference system (ANFIS). The proposed system has been validated for 28 test subjects (healthy and ACL-reconstructed). Results of ANFIS showed that the ambulation data can be used to distinguish subjects at different levels of recuperation after ACL reconstruction.

A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation

“…Eleven studies primarily used physics-based modeling. Integration of gyroscope data was combined with several drift compensation methods to estimate the sensor and body segment orientation in eight investigations 42 , 45 , 51 , 59 , 63 , 65 , 66 , 69 , and seven of these studies used the relative orientation between two segments to derive joint angles 42 , 45 , 51 , 59 , 63 , 65 , 66 . Only one study used musculoskeletal modeling, which estimated the GRF by simulating 25 artificial muscle-like actuators placed under each foot 70 .…”

Towards Out-of-Lab Anterior Cruciate Ligament Injury Prevention and Rehabilitation Assessment: A Review of Portable Sensing Approaches

“…Eleven studies primarily used physics-based modeling. Integration of gyroscope data was combined with several drift compensation methods to estimate the sensor and body segment orientation in eight investigations [37, 38, 41, 44-46, 68, 74], and seven of these studies used the relative orientation between two segments to derive joint angles [37, 41, 44-46, 68, 74]. Only one study used musculoskeletal modeling, which estimated the GRF by simulating 25 artificial muscle-like actuators placed under each foot [55].…”

“…Sensing approaches are IMU [26,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48], depth camera [27,[49][50][51][52][53][54][55], RGB camera [56][57][58][59], EMG [60,61], pressure insole [62], soft fabric sensor [63], and multiple sensors [28,[64][65][66][67][68][69][70][71][72][73][74].…”

“…Hip kinetics [28] Ankle kinetics [28] Anterior-posterior GRF [55] Medio-lateral GRF [55] Jump height [32] Hop distance [38] Human contour [56] Foot-ground contact [37, 38, 51, 72] [33, 34, 65] Step count [35] Injury risk rated by experts [47] Time after surgery [69] Recovery status [67,68] Highlighted cells are "primary parameters" associated with ACL in previous systematic reviews [10,11,19,20]. Green cells: primary parameters that have been assessed by included studies.…”

“…Twenty-one studies (43%) recruited patients following ACLR. The time from ACLR to experimental testing were within 3 months[33,34,40,60,61, 65], 3-12 months[28,29,32,34,39,40,46,48,62,64,65,[67][68][69]72], or more than 12 months[35,38,48,72] for the recruited patients. Eight studies (16%) recruited athletes at high risk of ACL injury, including basketball players…”