Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review.

After an ACL injury, rehabilitation consists of multiple phases, and progress between these phases is guided by subjective visual assessments of activities such as running, hopping, jump landing, etc. Estimation of objective kinetic measures like knee joint moments and GRF during assessment can help physiotherapists gain insights on knee loading and tailor rehabilitation protocols. Conventional methods deployed to estimate kinetics require complex, expensive systems and are limited to laboratory settings. Alternatively, multiple algorithms have been proposed in the literature to estimate kinetics from kinematics measured using only IMUs. However, the knowledge about their accuracy and generalizability for patient populations is still limited. Therefore, this article aims to identify the available algorithms for the estimation of kinetic parameters using kinematics measured only from IMUs and to evaluate their applicability in ACL rehabilitation through a comprehensive systematic review. The papers identified through the search were categorized based on the modelling techniques and kinetic parameters of interest, and subsequently compared based on the accuracies achieved and applicability for ACL patients during rehabilitation. IMUs have exhibited potential in estimating kinetic parameters with good accuracy, particularly for sagittal movements in healthy cohorts. However, several shortcomings were identified and future directions for improvement have been proposed, including extension of proposed algorithms to accommodate multiplanar movements and validation of the proposed techniques in diverse patient populations and in particular the ACL population.

Eating Event Recognition Using Accelerometer, Gyroscope, Piezoelectric, and Lung Volume Sensors.

In overcoming the worldwide problem of overweight and obesity, automatic dietary monitoring (ADM) is introduced as support in dieting practises. ADM aims to automatically, continuously, and objectively measure dimensions of food intake in a free-living environment. This could simplify the food registration process, thereby overcoming frequent memory, underestimation, and overestimation problems. In this study, an eating event detection sensor system was developed comprising a smartwatch worn on the wrist containing an accelerometer and gyroscope for eating gesture detection, a piezoelectric sensor worn on the jaw for chewing detection, and a respiratory inductance plethysmographic sensor consisting of two belts worn around the chest and abdomen for food swallowing detection. These sensors were combined to determine to what extent a combination of sensors focusing on different steps of the dietary cycle can improve eating event classification results. Six subjects participated in an experiment in a controlled setting consisting of both eating and non-eating events. Features were computed for each sensing measure to train a support vector machine model. This resulted in F1-scores of 0.82 for eating gestures, 0.94 for chewing food, and 0.58 for swallowing food.

A Sensor-Based Feedback Device Stimulating Daily Life Upper Extremity Activity in Stroke Patients: A Feasibility Study.

This study aims to evaluate the feasibility and explore the efficacy of the Arm Activity Tracker (AAT). The AAT is a device based on wrist-worn accelerometers that provides visual and tactile feedback to stimulate daily life upper extremity (UE) activity in stroke patients. METHODS: A randomised, crossover within-subject study was conducted in sub-acute stroke patients admitted to a rehabilitation centre. Feasibility encompassed (1) adherence: the dropout rate and the number of participants with insufficient AAT data collection; (2) acceptance: the technology acceptance model (range: 7-112) and (3) usability: the system usability scale (range: 0-100). A two-way ANOVA was used to estimate the difference between the baseline, intervention and control conditions for (1) paretic UE activity and (2) UE activity ratio. RESULTS: Seventeen stroke patients were included. A 29% dropout rate was observed, and two participants had insufficient data collection. Participants who adhered to the study reported good acceptance (median (IQR): 94 (77-111)) and usability (median (IQR): 77.5 (75-78.5)-). We found small to medium effect sizes favouring the intervention condition for paretic UE activity (η2G = 0.07, p = 0.04) and ratio (η2G = 0.11, p = 0.22). CONCLUSION: Participants who adhered to the study showed good acceptance and usability of the AAT and increased paretic UE activity. Dropouts should be further evaluated, and a sufficiently powered trial should be performed to analyse efficacy.

Centroidal Moment Pivot for ambulatory estimation of relative feet and CoM movement post stroke: Portable Gait Lab.

Measuring gait and balance recovery is necessary post stroke. In an earlier study, we developed a minimal three Inertial Measurement Units (IMUs) system called Portable Gait Lab (PGL). The PGL used the Centroidal Moment Pivot (CMP) assumption to estimate relative foot and centre of mass (CoM) positions, and thereby estimate gait parameters in healthy participants. In this study, we validate the feasibility of the PGL to track foot and CoM trajectory during gait in four persons with chronic stroke. Spatiotemporal gait and balance measures were estimated from the foot and CoM trajectories, and compared with the reference ForceShoes™. Each participant made at least 20 steps, and the PGL was able to track foot and CoM trajectories with a root mean square of the differences with the reference of 2.9 ± 0.2 cm and 4.6 ± 3.6 cm. The distances between either foot at the end of the walking task, and step lengths were estimated by PGL with an average error with the reference of 1.98 ± 2.2 cm and 7.8 ± 0.1 cm respectively across participants. We show that our approach was able to estimate spatiotemporal and balance parameters related to gait quality in a clinically useful manner. We recommend conducting further studies to study the feasibility of using the PGL system for variable gait patterns measured post stroke.

Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review.

Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6/13 running articles and 2/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson's correlation with an RPE (rate of perceived exertion) between r = 0.79 and r = 0.95. We recommend future effort toward the standardization of fatigue protocols and methods across articles in order to generalize fatigue identification results and increase the use of accelerometers to quantify physical fatigue in PE.

Smoothness metrics for reaching performance after stroke. Part 1: which one to choose?

BACKGROUND: Smoothness is commonly used for measuring movement quality of the upper paretic limb during reaching tasks after stroke. Many different smoothness metrics have been used in stroke research, but a 'valid' metric has not been identified. A systematic review and subsequent rigorous analysis of smoothness metrics used in stroke research, in terms of their mathematical definitions and response to simulated perturbations, is needed to conclude whether they are valid for measuring smoothness. Our objective was to provide a recommendation for metrics that reflect smoothness after stroke based on: (1) a systematic review of smoothness metrics for reaching used in stroke research, (2) the mathematical description of the metrics, and (3) the response of metrics to simulated changes associated with smoothness deficits in the reaching profile. METHODS: The systematic review was performed by screening electronic databases using combined keyword groups Stroke, Reaching and Smoothness. Subsequently, each metric identified was assessed with mathematical criteria regarding smoothness: (a) being dimensionless, (b) being reproducible, (c) being based on rate of change of position, and (d) not being a linear transform of other smoothness metrics. The resulting metrics were tested for their response to simulated changes in reaching using models of velocity profiles with varying reaching distances and durations, harmonic disturbances, noise, and sub-movements. Two reaching tasks were simulated; reach-to-point and reach-to-grasp. The metrics that responded as expected in all simulation analyses were considered to be valid. RESULTS: The systematic review identified 32 different smoothness metrics, 17 of which were excluded based on mathematical criteria, and 13 more as they did not respond as expected in all simulation analyses. Eventually, we found that, for reach-to-point and reach-to-grasp movements, only Spectral Arc Length (SPARC) was found to be a valid metric. CONCLUSIONS: Based on this systematic review and simulation analyses, we recommend the use of SPARC as a valid smoothness metric in both reach-to-point and reach-to-grasp tasks of the upper limb after stroke. However, further research is needed to understand the time course of smoothness measured with SPARC for the upper limb early post stroke, preferably in longitudinal studies.

Smoothness metric during reach-to-grasp after stroke: part 2. longitudinal association with motor impairment.

BACKGROUND: The cause of smoothness deficits as a proxy for quality of movement post stroke is currently unclear. Previous simulation analyses showed that spectral arc length (SPARC) is a valid metric for investigating smoothness during a multi-joint goal-directed reaching task. The goal of this observational study was to investigate how SPARC values change over time, and whether SPARC is longitudinally associated with the recovery from motor impairments reflected by the Fugl-Meyer motor assessment of the upper extremity (FM-UE) in the first 6 months after stroke. METHODS: Forty patients who suffered a first-ever unilateral ischemic stroke (22 males, aged 58.6 ± 12.5 years) with upper extremity paresis underwent kinematic and clinical measurements in weeks 1, 2, 3, 4, 5, 8, 12, and 26 post stroke. Clinical measures included amongst others FM-UE. SPARC was obtained by three-dimensional kinematic measurements using an electromagnetic motion tracking system during a reach-to-grasp movement. Kinematic assessments of 12 healthy, age-matched individuals served as reference. Longitudinal linear mixed model analyses were performed to determine SPARC change over time, compare smoothness in patients with reference values of healthy individuals, and establish the longitudinal association between SPARC and FM-UE scores. RESULTS: SPARC showed a significant positive longitudinal association with FM-UE (B: 31.73, 95%-CI: [27.27 36.20], P < 0.001), which encompassed significant within- and between-subject effects (B: 30.85, 95%-CI: [26.28 35.41], P < 0.001 and B: 50.59, 95%-CI: [29.97 71.21], P < 0.001, respectively). Until 5 weeks post stroke, progress of time contributed significantly to the increase in SPARC and FM-UE scores (P < 0.05), whereafter they levelled off. At group level, smoothness was lower in patients who suffered a stroke compared to healthy subjects at all time points (P < 0.05). CONCLUSIONS: The present findings show that, after stroke, recovery of smoothness in a multi-joint reaching task and recovery from motor impairments are longitudinally associated and follow a similar time course. This suggests that the reduction of smoothness deficits quantified by SPARC is a proper objective reflection of recovery from motor impairment, as reflected by FM-UE, probably driven by a common underlying process of spontaneous neurological recovery early post stroke.

Glucose Regulation Beyond HbA1c in Type 2 Diabetes Treated With Insulin: Real-World Evidence From the DIALECT-2 Cohort.

OBJECTIVE: To investigate glucose variations associated with glycated hemoglobin (HbA1c) in insulin-treated patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: Patients included in Diabetes and Lifestyle Cohort Twente (DIALECT)-2 (n = 79) were grouped into three HbA1c categories: low, intermediate, and high (≤53, 54-62, and ≥63 mmol/mol or ≤7, 7.1-7.8, and ≥7.9%, respectively). Blood glucose time in range (TIR), time below range (TBR), time above range (TAR), glucose variability parameters, day and night duration, and frequency of TBR and TAR episodes were determined by continuous glucose monitoring (CGM) using the FreeStyle Libre sensor and compared between HbA1c categories. RESULTS: CGM was performed for a median (interquartile range) of 10 (7-12) days/patient. TIR was not different for low and intermediate HbA1c categories (76.8% [68.3-88.2] vs. 76.0% [72.5.0-80.1]), whereas in the low category, TBR was higher and TAR lower (7.7% [2.4-19.1] vs. 0.7% [0.3-6.1] and 8.2% [5.7-17.6] vs. 20.4% [11.6-27.0], respectively, P < 0.05). Patients in the highest HbA1c category had lower TIR (52.7% [40.9-67.3]) and higher TAR (44.1% [27.8-57.0]) than the other HbA1c categories (P < 0.05), but did not have less TBR during the night. All patients had more (0.06 ± 0.06/h vs. 0.03 ± 0.03/h; P = 0.002) and longer (88.0 [45.0-195.5] vs. 53.4 [34.4-82.8] minutes; P < 0.001) TBR episodes during the night than during the day. CONCLUSIONS: In this study, a high HbA1c did not reduce the occurrence of nocturnal hypoglycemia, and low HbA1c was not associated with the highest TIR. Optimal personalization of glycemic control requires the use of newer tools, including CGM-derived parameters.

Towards Machine Learning-Based Detection of Running-Induced Fatigue in Real-World Scenarios: Evaluation of IMU Sensor Configurations to Reduce Intrusiveness.

Physical fatigue is a recurrent problem in running that negatively affects performance and leads to an increased risk of being injured. Identification and management of fatigue helps reducing such negative effects, but is presently commonly based on subjective fatigue measurements. Inertial sensors can record movement data continuously, allowing recording for long durations and extensive amounts of data. Here we aimed to assess if inertial measurement units (IMUs) can be used to distinguish between fatigue levels during an outdoor run with a machine learning classification algorithm trained on IMU-derived biomechanical features, and what is the optimal configuration to do so. Eight runners ran 13 laps of 400 m on an athletic track at a constant speed with 8 IMUs attached to their body (feet, tibias, thighs, pelvis, and sternum). Three segments were extracted from the run: laps 2-4 (no fatigue condition, Rating of Perceived Exertion (RPE) = 6.0 ± 0.0); laps 8-10 (mild fatigue condition, RPE = 11.7 ± 2.0); laps 11-13 (heavy fatigue condition, RPE = 14.2 ± 3.0), run directly after a fatiguing protocol (progressive increase of speed until RPE ≥ 16) that followed lap 10. A random forest classification algorithm was trained with selected features from the 400 m moving average of the IMU-derived accelerations, angular velocities, and joint angles. A leave-one-subject-out cross validation was performed to assess the optimal combination of IMU locations to detect fatigue and selected sensor configurations were considered. The left tibia was the most recurrent sensor location, resulting in accuracies ranging between 0.761 (single left tibia location) and 0.905 (all IMU locations). These findings contribute toward a balanced choice between higher accuracy and lower intrusiveness in the development of IMU-based fatigue detection devices in running.

Foot progression angle estimation using a single foot-worn inertial sensor.

BACKGROUND: The foot progression angle is an important measure used to help patients reduce their knee adduction moment. Current measurement systems are either lab-bounded or do not function in all environments (e.g., magnetically distorted). This work proposes a novel approach to estimate foot progression angle using a single foot-worn inertial sensor (accelerometer and gyroscope). METHODS: The approach uses a dynamic step frame that is recalculated for the stance phase of each step to calculate the foot trajectory relative to that frame, to minimize effects of drift and to eliminate the need for a magnetometer. The foot progression angle (FPA) is then calculated as the angle between walking direction and the dynamic step frame. This approach was validated by gait measurements with five subjects walking with three gait types (normal, toe-in and toe-out). RESULTS: The FPA was estimated with a maximum mean error of ~ 2.6° over all gait conditions. Additionally, the proposed inertial approach can significantly differentiate between the three different gait types. CONCLUSION: The proposed approach can effectively estimate differences in FPA without requiring a heading reference (magnetometer). This work enables feedback applications on FPA for patients with gait disorders that function in any environment, i.e. outside of a gait lab or in magnetically distorted environments.

Quantifying Pathological Synergies in the Upper Extremity of Stroke Subjects With the Use of Inertial Measurement Units: A Pilot Study.

BACKGROUND: Stroke is one of the main causes of disability in the world, causing loss of motor function on mainly one side of the body. A proper assessment of motor function is required to help to direct and evaluate therapy. Assessment is currently performed by therapists using observer-based standardized clinical assessment protocols. Sensor-based technologies can be used to objectively quantify the presence and severity of motor impairments in stroke patients. METHODS: In this work, a minimally obstructive distributed inertial sensing system, intended to measure kinematics of the upper extremity, was developed and tested in a pilot study, where 10 chronic stroke subjects performed the arm-related tasks from the Fugl-Meyer Assessment protocol with the affected and non-affected side. RESULTS: The pilot study showed that the developed distributed measurement system was adequately sensitive to show significant differences in stroke subjects' arm postures between the affected and non-affected side. The presence of pathological synergies can be analysed using the measured joint angles of the upper limb segments, that describe the movement patterns of the subject. CONCLUSION: Features measured by the system vary from the assessed FMA-UE sub-score showing its potential to provide more detailed clinical information.

Portable Gait Lab: Estimating Over-Ground 3D Ground Reaction Forces Using Only a Pelvis IMU.

As an alternative to force plates, an inertial measurement unit (IMU) at the pelvis can offer an ambulatory method for measuring total center of mass (CoM) accelerations and, thereby, the ground reaction forces (GRF) during gait. The challenge here is to estimate the 3D components of the GRF. We employ a calibration procedure and an error state extended Kalman filter based on an earlier work to estimate the instantaneous 3D GRF for different over-ground walking patterns. The GRF were then expressed in a body-centric reference frame, to enable an ambulatory setup not related to a fixed global frame. The results were validated with ForceShoesTM, and the average error in estimating instantaneous shear GRF was 5.2 ± 0.5% of body weight across different variable over-ground walking tasks. The study shows that a single pelvis IMU can measure 3D GRF in a minimal and ambulatory manner during over-ground gait.

Low Physical Activity in Patients with Complicated Type 2 Diabetes Mellitus Is Associated with Low Muscle Mass and Low Protein Intake.

OBJECTIVE: In order to promote physical activity (PA) in patients with complicated type 2 diabetes, a better understanding of daily movement is required. We (1) objectively assessed PA in patients with type 2 diabetes, and (2) studied the association between muscle mass, dietary protein intake, and PA. Methods: We performed cross-sectional analyses in all patients included in the Diabetes and Lifestyle Cohort Twente (DIALECT) between November 2016 and November 2018. Patients were divided into four groups: <5000, 5000-6999, 7000-9999, ≥ 10,000 steps/day. We studied the association between muscle mass (24 h urinary creatinine excretion rate, CER) and protein intake (by Maroni formula), and the main outcome variable PA (steps/day, Fitbit Flex device) using multivariate linear regression analyses. RESULTS: In the 217 included patients, the median steps/day were 6118 (4115-8638). Of these patients, 48 patients (22%) took 7000-9999 steps/day, 37 patients (17%) took ≥ 10,000 steps/day, and 78 patients (36%) took <5000 steps/day. Patients with <5000 steps/day had, in comparison to patients who took ≥10,000 steps/day, a higher body mass index (BMI) (33 ± 6 vs. 30 ± 5 kg/m2, p = 0.009), lower CER (11.7 ± 4.8 vs. 14.8 ± 3.8 mmol/24 h, p = 0.001), and lower protein intake (0.84 ± 0.29 vs. 1.08 ± 0.22 g/kg/day, p < 0.001). Both creatinine excretion (ß = 0.26, p < 0.001) and dietary protein intake (ß = 0.31, p < 0.001) were strongly associated with PA, which remained unchanged after adjustment for potential confounders. CONCLUSIONS: Prevalent insufficient protein intake and low muscle mass co-exist in obese patients with low physical activity. Dedicated intervention studies are needed to study the role of sufficient protein intake and physical activity in increasing or maintaining muscle mass in patients with type 2 diabetes.

Portable Gait Lab: Tracking Relative Distances of Feet and CoM Using Three IMUs.

Ambulatory estimation of gait and balance parameters requires knowledge of relative feet and centre of mass (CoM) positions. Inertial measurement units (IMU) placed on each foot, and on the pelvis are useful in tracking these segments over time, but cannot track the relative distances between these segments. Further, drift due to strapdown inertial navigation results in erroneous relative estimates of feet and CoM positions after a few steps. In this study, we track the relative distances using the assumptions of the Centroidal Moment Pivot (CMP) theory. An Extended Kalman filter approach was used to fuse information from different sources: strapdown inertial navigation, commonly used constraints such as zero velocity updates, and relative segment distances from the CMP assumption; to eventually track relative feet and CoM positions. These estimates were expressed in a reference frame defined by the heading of each step. The validity of this approach was tested on variable gait. The step lengths and step widths were estimated with an average absolute error of 4.6±1.5 cm and 3.8±1.5 cm respectively when compared against the reference VICON©. Additionally, we validated the relative distances of the feet and the CoM, and further, show that the approach proves useful in identifying asymmetric gait patterns. We conclude that a three IMU approach is feasible as a portable gait lab for ambulatory measurement of foot and CoM positions in daily life.

Estimation of Relative Hand-Finger Orientation Using a Small IMU Configuration.

Relative orientation estimation between the hand and its fingers is important in many applications, such as virtual reality (VR), augmented reality (AR) and rehabilitation. It is still quite a big challenge to do the estimation by only exploiting inertial measurement units (IMUs) because of the integration drift that occurs in most approaches. When the hand is functionally used, there are many instances in which hand and finger tips move together, experiencing almost the same angular velocities, and in some of these cases, almost the same accelerations are measured in different 3D coordinate systems. Therefore, we hypothesize that relative orientations between the hand and the finger tips can be adequately estimated using 3D IMUs during such designated events (DEs) and in between these events. We fused this extra information from the DEs and IMU data with an extended Kalman filter (EKF). Our results show that errors in relative orientation can be smaller than five degrees if DEs are constantly present and the linear and angular movements of the whole hand are adequately rich. When the DEs are partially available in a functional water-drinking task, the orientation error is smaller than 10 degrees.

Portable Gait Lab: Estimating 3D GRF Using a Pelvis IMU in a Foot IMU Defined Frame.

Ground Reaction Forces (GRF) during gait are measured using expensive laboratory setups such as in-floor or treadmill force plates. Ambulatory measurement of GRF using wearables enables remote monitoring of gait and balance. Here, we propose using an Inertial Measurement Unit (IMU) mounted on the pelvis to estimate the GRF during gait in daily life. Calibration procedures and an Error State Extended Kalman filter (EEKF) were used to transform the accelerations at the center of mass (CoM) to the 3D GRF. The instantaneous 3D GRF was estimated for different overground walking patterns and compared with the 3D GRF measured using the reference ForceShoe™ system. Furthermore, we introduce a changing reference frame called the current step frame that followed the direction of each step made. The frame was defined using movement of the feet, and the estimated GRF were expressed in this new frame. This allowed direct comparison and validation with the reference. The mean and standard deviation of error between the estimated instantaneous 3D GRF and the reference, normalized against the range of the reference, was 12.1 ± 3.3% across all walking tasks, in the horizontal plane. The error margins show that a single pelvis IMU could be a minimal and ambulatory sensing alternative for estimating the instantaneous 3D components of GRF during overground gait.

Time Coherent Full-Body Poses Estimated Using Only Five Inertial Sensors: Deep versus Shallow Learning.

Full-body motion capture typically requires sensors/markers to be placed on each rigid body segment, which results in long setup times and is obtrusive. The number of sensors/markers can be reduced using deep learning or offline methods. However, this requires large training datasets and/or sufficient computational resources. Therefore, we investigate the following research question: "What is the performance of a shallow approach, compared to a deep learning one, for estimating time coherent full-body poses using only five inertial sensors?". We propose to incorporate past/future inertial sensor information into a stacked input vector, which is fed to a shallow neural network for estimating full-body poses. Shallow and deep learning approaches are compared using the same input vector configurations. Additionally, the inclusion of acceleration input is evaluated. The results show that a shallow learning approach can estimate full-body poses with a similar accuracy (~6 cm) to that of a deep learning approach (~7 cm). However, the jerk errors are smaller using the deep learning approach, which can be the effect of explicit recurrent modelling. Furthermore, it is shown that the delay using a shallow learning approach (72 ms) is smaller than that of a deep learning approach (117 ms).

Requirements of an Application to Monitor Diet, Physical Activity and Glucose Values in Patients with Type 2 Diabetes: The Diameter.

Adherence to a healthy diet and regular physical activity are two important factors in sufficient type 2 diabetes mellitus management. It is recognized that the traditional treatment of outpatients does not meet the requirements for sufficient lifestyle management. It is hypothesised that a personalized diabetes management mHealth application can help. Such an application ideally measures food intake, physical activity, glucose values, and medication use, and then integrates this to provide patients and healthcare professionals insight in these factors, as well as the effect of lifestyle on glucose values in daily life. The lifestyle data can be used to give tailored coaching to improve adherence to lifestyle recommendations and medication use. This study describes the requirements for such an application: the Diameter. An iterative mixed method design approach is used that consists of a cohort study, pilot studies, literature search, and expert meetings. The requirements are defined according to the Function and events, Interactions and usability, Content and structure and Style and aesthetics (FICS) framework. This resulted in 81 requirements for the dietary (n = 37), activity and sedentary (n = 15), glycaemic (n = 12), and general (n = 17) parts. Although many applications are currently available, many of these requirements are not implemented. This stresses the need for the Diameter as a new personalized diabetes application.

Portable Gait Lab: Zero Moment Point for Minimal Sensing of Gait.

Ambulatory sensing of gait kinematics using inertial measurement units (IMUs) usually uses sensor fusion filters. These algorithms require measurement updates to reduce drift between segments. A full body IMU suit can use biomechanical relations between body segments to solve this. However, when minimising the sensor set, we lose a lot of this information. In this study, we explore the assumptions of zero moment point (ZMP) as a possible source of measurement updates for the sensor fusion filters. ZMP is otherwise utilised for humanoid gait in robots. In this study, first, the relation between the ZMP and centre of pressure (CoP) is studied using a GRAIL system, consisting of opto-kinetic measurements. We find that the mean distance over the gait cycle between ZMP and CoP is 10.5±1.2% of the foot length. Following this, we show how these results could be used to improve measurements in a minimal IMU based sensing setup.