Luke Wicent Sy

Background. Knee and hip arthroplasty are two of the most frequently performed procedures in orthopaedic surgery. They are associated with positive patient-reported outcomes and significant improvements in quality of life for patients. Despite this, there may be room for further progress by quantifying functional improvements with gait analysis. Our study therefore aims to characterise the disease-specific gait pattern of participants with knee and hip osteoarthritis undergoing total joint replacement using a single chest-based wearable sensor. Methods. Twenty-nine participants awaiting total hip replacement and 28 participants awaiting total knee replacement underwent three-dimensional motion analysis with inertial wearable sensors. These gait metrics were then compared with 28 healthy controls of similar ages. Differences in gait metrics were evaluated using a T-test. The participants were recruited through a single centre to participate in this cross-sectional observational study. Participants with osteoarthritis severity sufficient to warrant surgical intervention were considered for inclusion in our study. The participants were instructed to walk 15–120 m in a hospital environment while fitted with a chest-based wearable sensor. Results. In total, three domains were evaluated, including spatiotemporal, variability and asymmetry parameters. There were marked variations in the gait asymmetry parameters and step length variation in both the hip and knee osteoarthritis patients compared with the healthy controls. The magnitude of gait deterioration in terms of step length asymmetry was greater on average in the hip osteoarthritis group than the knee group. The hip osteoarthritis (+180%, p < 0.001) and knee osteoarthritis (+129%, p = 0.001) groups demonstrated marked differences in step length asymmetry. Discussion. A single chest-based sensor was found to be capable of detecting pathological gait signatures in osteoarthritis patients when compared with age-matched controls. Future studies should compare pre- and postoperative changes to disease-specific gait impairments to validate the use of wearable sensors as a clinical adjunct.

Background: Outcome measurement in lumbar surgery is traditionally performed using patient questionnaires that may be limited by subjectivity. Objective gait analysis may supplement patient assessment but must be clinically viable. We assessed gait metrics in lumbar spine patients pre- and postoperatively using a small and lightweight wearable sensor. Methods: This was a prospective observational study with intervention including 12 patients undergoing lumbar spine surgery and 24 healthy controls matched based on age and sex. All the subjects underwent gait analysis using the single-point wearable MetaMotionC sensor. The lumbar spine patients also completed traditional patient questionnaires including the Oswestry Disability Index (ODI). Results: The ODI score significantly improved in the patients from the baseline to six weeks postoperatively (42.4 to 22.8; p = 0.01). Simultaneously, the patients demonstrated significant improvements in gait asymmetry (asymmetry in step length, swing time, single support time, and double support time, by 17.4–60.3%; p ≤ 0.039) and variability (variability in gait velocity, step time, step length, stance time, swing time, single support time, and double support time, by 21.0–65.8%; p ≤ 0.023). After surgery, changes in most spatiotemporal (gait velocity, step length, stance time, swing time, and single limb support time) and asymmetry (asymmetry in step time, stance time, swing time, and single limb support time) metrics correlated strongly (magnitude of r = 0.581–0.914) and significantly (p ≤ 0.037) with changes in the ODI. Conclusions: Gait analysis using a single-point wearable sensor can demonstrate objective evidence of recovery in lumbar spine patients after surgery. This may be used as a routine pre- and postoperative assessment during scheduled visits to the clinic.

Soft actuators (SAs) have been used in many compliant robotic structure and wearable devices, due to their safe interaction with the wearers. Despite advances, the capability of current SAs is limited by scalability, high hysteresis, and slow responses. In this paper, a new class of soft, scalable, and high-aspect ratio fiber-reinforced hydraulic SAs is introduced. The new SA uses a simple fabrication process of insertion where a hollow elastic rubber tube is directly inserted into a constrained hollow coil, eliminating the need for the manual wrapping of an inextensible fiber around a long elastic structure. To provide high adaptation to the user skin for wearable applications, the new SAs are integrated into flexible fabrics to form a wearable fabric sleeve. To monitor the SA elongation, a soft liquid metal-based fabric piezoresistive sensor is also developed. To capture the nonlinear hysteresis of the SA, a novel asymmetric hysteresis model which only requires five model parameters in its structure is developed and experimentally validated. The new SAs-driven wearable robotic sleeve is scalable, highly flexible, and lightweight. It can also produce a large amount of force of around 23 N per muscle at around 30% elongation, to provide useful assistance to the human upper limbs. Experimental results show that the soft fabric sleeve can augment a user’s performance when working against a load, evidenced by a significant reduction on the muscular effort, as monitored by electromyogram (EMG) signals. The performance of the developed SAs, soft fabric sleeve, soft liquid metal fabric sensor, and nonlinear hysteresis model reveal that they can effectively modulate the level of assistance for the wearer. The new technologies obtained from this work can be potentially implemented in emerging assistive applications, such as rehabilitation, defense, and industry.

Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis Callum Betteridge, Ralph J. Mobbs, R. Dineth Fonseka, Pragadesh Natarajan, Daniel Ho, Wen Jie Choy, Luke W. Sy, Nina Pell

Goal: Gait monitoring is useful for diagnosing movement disorders or assessing surgical outcomes. This paper presents an algorithm for estimating pelvis, thigh, shank, and foot kinematics during walking using only two or three wearable inertial sensors. Methods: The algorithm makes novel use of a Lie-group-based extended Kalman filter. The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, zero-velocity update, and flat-floor assumption), and constraint update (hinged knee and ankle joints, constant leg lengths). Results: The inertial motion capture algorithm was extensively evaluated on two datasets showing its performance against two standard benchmark approaches in optical motion capture (i.e., plug-in gait (commonly used in gait analysis) and a kinematic fit (commonly used in animation, robotics, and musculoskeleton simulation)), giving insight into the similarity and differences between the said approaches used in different application areas. The overall mean body segment position (relative to mid-pelvis origin) and orientation error magnitude of our algorithm ( n=14 participants) for free walking was 5.93 ± 1.33 cm and 13.43 ±1.89 ^° when using three IMUs placed on the feet and pelvis, and 6.35 ± 1.20 cm and 12.71 ±1.60 ^° when using only two IMUs placed on the feet. Conclusion: The algorithm was able to track the joint angles in the sagittal plane for straight walking well, but requires improvement for unscripted movements (e.g., turning around, side steps), especially for dynamic movements or when considering clinical applications. Significance: This work has brought us closer to comprehensive remote gait monitoring using IMUs on the shoes. The low computational cost also suggests that it can be used in real-time with gait assistive devices.

An engineer’s perspective on the mechanisms and applications of wearable inertial sensors Luke Wicent Sy

Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were 7.6±2.6∘ and 6.6±2.7∘, respectively, while the correlation coefficients were 0.95±0.03 and 0.87±0.16, respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels (σ=0.2 m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.