Richard M. Kwasnicki

Gait Parameter Estimation From a Miniaturized Ear-Worn Sensor Using Singular Spectrum Analysis and Longest Common Subsequence

This paper presents a new approach to gait analysis and parameter estimation from a single miniaturized ear-worn sensor embedded with a triaxial accelerometer. Singular spectrum analysis combined with the longest common subsequence algorithm has been used as a basis for gait parameter estimation. It incorporates information from all axes of the accelerometer to estimate parameters including swing, stance, and stride times. Rather than only using local features of the raw signals, the periodicity of the signals is also taken into account. The hypotheses tested by this study include: 1) how accurate is the ear-worn sensor in terms of gait parameter extraction compared to the use of an instrumented treadmill; 2) does the ear-worn sensor provide a feasible option for assessment and quantification of gait pattern changes. Key gait events for normal subjects such as heel contact and toe off are validated with a high-speed camera, as well as a force-plate instrumented treadmill. Ten healthy adults walked for 20 min on a treadmill with an increasing incline of 2% every 2 min. The upper and lower limits of the absolute errors using 95% confidence intervals for swing, stance, and stride times were obtained as 35.5 ±3.99 ms, 36.9 ±3.84 ms, and 17.9 ±2.29 ms, respectively.

A Comparison of Skill Acquisition and Transfer in Single Incision and Multi-port Laparoscopic Surgery

OBJECTIVES Single incision laparoscopic surgery (SILS) offers a scar-less approach to cholecystectomy. We conducted a cadaveric randomized crossover study to compare the novice learning curves for multiport laparoscopic cholecystectomy (LC) and single incision laparoscopic cholecystectomy (SILC), and to investigate the acquisition of transferable skills. PARTICIPANTS Twenty medical students were randomized into SILS or LC groups. METHODS After baseline assessment and cognitive learning modules, groups completed 5 cadaveric porcine cholecystectomies in their designated modality, followed by one using the other approach. Performance was assessed using a validated surgical assessment device (ICSAD) and by expert video analysis with generic and procedure-specific rating scales [modified global rating scale (mGRS) and procedure-specific rating scales (PSRS)]. RESULTS Analysis of the first case revealed significant differences between LC and SILS groups for time-taken (median 46.00 vs 68.19 min, p = 0.019), and path length (216 vs 348 m, p = 0.034). Intergroup analysis of the remaining group cases showed no difference for any of the performance metrics. Outlying performance of the 4th case in the LC group rendered learning curve comparison unviable. At crossover, performance of the SILS group on their LC compared with the 5th LC performed by the LC group showed no significant difference. However, comparing the LC groups SILC to the 5th SILC performed by the SILS group showed significant difference for all performance metrics (p < 0.05). CONCLUSIONS This study suggests that the difference between novice performance for SILC and LC becomes negligible after the first procedure. Furthermore, dedicated SILC training appears to develop competencies for both SILC and LC, therefore its addition to the early surgical curriculum is likely to extend the access of SILC to patients without reducing multiport laparoscopic skill acquisition.

A pilot study to determine whether using a lightweight, wearable micro-camera improves dietary assessment accuracy and offers information on macronutrients and eating rate.

A major limitation in nutritional science is the lack of understanding of the nutritional intake of free-living people. There is an inverse relationship between accuracy of reporting of energy intake by all current nutritional methodologies and body weight. In this pilot study we aim to explore whether using a novel lightweight, wearable micro-camera improves the accuracy of dietary intake assessment. Doubly labelled water (DLW) was used to estimate energy expenditure and intake over a 14-d period, over which time participants (n 6) completed a food diary and wore a micro-camera on 2 of the days. Comparisons were made between the estimated energy intake from the reported food diary alone and together with the images from the micro-camera recordings. There was an average daily deficit of 3912 kJ using food diaries to estimate energy intake compared with estimated energy expenditure from DLW (P=0·0118), representing an under-reporting rate of 34 %. Analysis of food diaries alone showed a significant deficit in estimated daily energy intake compared with estimated intake from food diary analysis with images from the micro-camera recordings (405 kJ). Use of the micro-camera images in conjunction with food diaries improves the accuracy of dietary assessment and provides valuable information on macronutrient intake and eating rate. There is a need to develop this recording technique to remove user and assessor bias.

Predicting Free-Living Energy Expenditure Using a Miniaturized Ear-Worn Sensor: An Evaluation Against Doubly Labeled Water

Accurate estimation of daily total energy expenditure (EE) is a prerequisite for assisted weight management and assessing certain health conditions. The use of wearable sensors for predicting free-living EE is challenged by consistent sensor placement, user compliance, and estimation methods used. This paper examines whether a single ear-worn accelerometer can be used for EE estimation under free-living conditions. An EE prediction model was first derived and validated in a controlled setting using healthy subjects involving different physical activities. Ten different activities were assessed showing a tenfold cross validation error of 0.24. Furthermore, the EE prediction model shows a mean absolute deviation below 1.2 metabolic equivalent of tasks. The same model was applied to a free-living setting with a different population for further validation. The results were compared against those derived from doubly labeled water. In free-living settings, the predicted daily EE has a correlation of 0.74, p = 0.008, and a MAD of 27 kcal/day. These results demonstrate that laboratory-derived prediction models can be used to predict EE under free-living conditions.

Enhanced recovery after surgery: Current research insights and future direction

Since the concept of enhanced recovery after surgery (ERAS) was introduced in the late 1990s the idea of implementing specific interventions throughout the peri-operative period to improve patient recovery has been proven to be beneficial. Minimally invasive surgery is an integral component to ERAS and has dramatically improved post-operative outcomes. ERAS can be applicable to all surgical specialties with the core generic principles used together with added specialty specific interventions to allow for a comprehensive protocol, leading to improved clinical outcomes. Diffusion of ERAS into mainstream practice has been hindered due to minimal evidence to support individual facets and lack of method for monitoring and encouraging compliance. No single outcome measure fully captures recovery after surgery, rather multiple measures are necessary at each stage. More recently the pre-operative period has been the target of a number of strategies to improve clinical outcomes, described as prehabilitation. Innovation of technology in the surgical setting is also providing opportunities to overcome the challenges within ERAS, e.g., the use of wearable activity monitors to record information and provide feedback and motivation to patients peri-operatively. Both modernising ERAS and providing evidence for key strategies across specialties will ultimately lead to better, more reliable patient outcomes.

Can virtual reality simulation be used for advanced bariatric surgical training

INTRODUCTION Laparoscopic bariatric surgery is a safe and effective way of treating morbid obesity. However, the operations are technically challenging and training opportunities for junior surgeons are limited. This study aims to assess whether virtual reality (VR) simulation is an effective adjunct for training and assessment of laparoscopic bariatric technical skills. METHODS Twenty bariatric surgeons of varying experience (Five experienced, five intermediate, and ten novice) were recruited to perform a jejuno-jejunostomy on both cadaveric tissue and on the bariatric module of the Lapmentor VR simulator (Simbionix Corporation, Cleveland, OH). Surgical performance was assessed using validated global rating scales (GRS) and procedure specific video rating scales (PSRS). Subjects were also questioned about the appropriateness of VR as a training tool for surgeons. RESULTS Construct validity of the VR bariatric module was demonstrated with a significant difference in performance between novice and experienced surgeons on the VR jejuno-jejunostomy module GRS (median 11-15.5; P = .017) and PSRS (median 11-13; P = .003). Content validity was demonstrated with surgeons describing the VR bariatric module as useful and appropriate for training (mean Likert score 4.45/7) and they would highly recommend VR simulation to others for bariatric training (mean Likert score 5/7). Face and concurrent validity were not established. CONCLUSION This study shows that the bariatric module on a VR simulator demonstrates construct and content validity. VR simulation appears to be an effective method for training of advanced bariatric technical skills for surgeons at the start of their bariatric training. However, assessment of technical skills should still take place on cadaveric tissue.

Wearable Tissue Oxygenation Monitoring Sensor and a Forearm Vascular Phantom Design for Data Validation

Photoplethysmography (PPG) is a well established method of measuring Heart Rate Variability (HRV) and blood oxygen saturation (SpO2) at the fingers, forehead or other areas of the body where pulsatile flow is present. However, obtaining reliable tissue oxygen saturation (StO2) from optical devices is more challenging due to a number of factors including motion and signal-to-noise ratio. Instrumentation of such devices as miniaturised wearable platforms would allow the device to be worn freely by patients in hospitals or at home. The purposes of this paper are to present: 1) a bespoke, low power StO2 sensor, 2) preliminary comparison to a commercially available photospectroscopy and laser Doppler machine (Oxygen 2 See, Medizintechnik, LEA, Germany) using a pressure cuff forearm ischaemia model, and 3) validation of fluid (blood) flow/relative haemoglobin measurements using a novel forearm phantom.

Assessing functional mobility after lower limb reconstruction: a psychometric evaluation of a sensor-based mobility score.

OBJECTIVE To develop and validate a robust, objective mobility assessment tool, Hamlyn Mobility Score (HMS), using a wearable motion sensor. BACKGROUND Advances in reconstructive techniques allow more limbs to be salvaged. However, evidence demonstrating superior long-term outcomes compared with amputation is unavailable. Lack of access to quality regular functional mobility status may be preventing patients and health care staff from optimizing rehabilitation programs and evaluating the reconstructive services. METHODS In this prospective cohort study, 20 patients undergoing lower limb reconstruction and 10 age-matched controls were recruited. All subjects completed the HMS activity protocol twice under different instructors at 3 months postoperatively, and again at 6 months, while wearing an ear-worn accelerometer. Demographic and clinical data were also collected including a short-form health survey (SF-36). HMS parameters included standard test metrics and additional kinematic features extracted from accelerometer data. A psychometric evaluation was conducted to ascertain reliability and validity. RESULTS The HMS demonstrated excellent reliability (intraclass correlation coefficient >0.90, P < 0.001) and internal consistency (Cronbach α = 0.897). Concurrent validity was demonstrated by correlation between HMS and SF-36 scores (Spearman ρ = 0.666, P = 0.005). Significant HMS differences between healthy subjects and patients, stratified according to fracture severity, were shown (Kruskal-Wallis nonparametric 1-way analysis of variance, χ = 21.5, P < 0.001). The HMS was 50% more responsive to change than SF-36 (effect size: 1.49 vs 0.99). CONCLUSIONS The HMS shows satisfactory reliability and validity and may provide a platform to support adaptable, personalized rehabilitation and enhanced service evaluation to facilitate optimal patient outcomes.

A high fidelity model for single-incision laparoscopic cholecystectomy

Single-incision laparoscopic surgery (SILS) is a safe approach for cholecystectomy, with the potential to minimise the iatrogenic trauma sustained from the operation. However, a number of reports show SILS to be technically challenging and as such there is expected to be a significant learning curve for expert surgeons adopting the new technique, as well as for junior surgical trainees. There are inherent risks to patient safety associated with practicing and developing new skills in a real-life theatre environment. However, thus far, there have been no realistic SILS training models available. We tested the feasibility of conducting SILS cholecystectomies on a cadaveric porcine model with standard operating equipment, which may provide a platform to facilitate safe training and assessment protocols. In this paper we provide an account of the training model technique, and review the literature surrounding SILS training and performance evaluation.

Motion Reconstruction from Sparse Accelerometer Data Using PLSR

Detailed motion reconstruction is a prerequisite of biomotion analysis and physical function assessment for a variety of scenarios. For example, biomechanical analysis can be used to assess physical activity to diagnose pathological conditions, to provide an objective measure of biomechanics for peri-operative care, and to monitor patients with mobility issues. Unfortunately, current motion capture systems cannot perform biomechanical analysis continuously in the patients natural environment. In this paper, a pose estimation scheme from a sparse network of accelerometer-based wearable sensors, which does not impose restrictions upon the patients daily life, is presented. In the proposed method, a marker-based motion capture system is used for acquiring the 3D motion data, and partial least squares regression (PLSR) is used to establish the implicit model between 3D body pose and the wearable sensor measurements. A linear constant velocity process model and measurement model are designed and a Kalman filter is then deployed to estimate the posture. Experimental results demonstrate the strength of the technique and how it can be used to estimate detailed 3D motion from a sparse set of sensors.