New generation of wearable goniometers for motion capture systems

A Novel IMU-Based System for Work-Related Musculoskeletal Disorders Risk Assessment

This study introduces a novel wearable Inertial Measurement Unit (IMU)-based system for an objective and comprehensive assessment of Work-Related Musculoskeletal Disorders (WMSDs), thus enhancing workplace safety. The system integrates wearable technology with a user-friendly interface, providing magnetometer-free orientation estimation, joint angle measurements, and WMSDs risk evaluation. Tested in a cable manufacturing facility, the system was evaluated with ten female employees. The evaluation involved work cycle identification, inter-subject comparisons, and benchmarking against standard WMSD risk assessments like RULA, REBA, Strain Index, and Rodgers Muscle Fatigue Analysis. The evaluation demonstrated uniform joint patterns across participants (ICC=0.72±0.23) and revealed a higher occurrence of postures warranting further investigation, which is not easily detected by traditional methods such as RULA. The experimental results showed that the proposed system's risk assessments closely aligned with the established methods and enabled detailed and targeted risk assessments, pinpointing specific bodily areas for immediate ergonomic interventions. This approach not only enhances the detection of ergonomic risks but also supports the development of personalized intervention strategies, addressing common workplace issues such as tendinitis, low back pain, and carpal tunnel syndrome. The outcomes highlight the system's sensitivity and specificity in identifying ergonomic hazards. Future efforts should focus on broader validation and exploring the relative influence of various WMSDs risk factors to refine risk assessment and intervention strategies for improved applicability in occupational health.

Three-Directional Spacer-Knitted Piezoresistant Strain and Pressure Sensor for Electronic Integration and On-Body Applications

The advancement of smart textiles has resulted in significant development in wearable textile sensors and offers novel interfaces to sense physical movements in daily life. Knitting, as a traditional textile fabrication method, is being used in promising ways to realize fully seamless fabrication and unobtrusive sensing in wearable textile applications. However, current flat-knitted sensors can sense strain only in the horizontal plane. This research presents a novel fully machine-knitted spacer piezoresistive sensor structure with a three-directional sensing ability that can detect both the pressure in the vertical direction and the strain in the warp/weft direction. Besides, it can sense the pressure under 1 kPa, which is critical in comfortable on-body interaction, one-piece integration, and wearable applications. Three sizes spacer-knitted sensors are evaluated in terms of their mechanical performance, stability cycles, and reaction to external factors such as sweat, laundering, etc. Then, the effect of material choice on sensor performance is evaluated and the rationale behind the use of different materials is summarized. Specifically, this research presents a detailed evaluation of the applications with both a single sensor and multiple sensor arrays for fine and gross motion sensing in several scenarios. The testing results demonstrate a fully machine-knitted piezoresistive sensor that can detect multidirectional motions (vertical, warp, and weft directions). In addition, this knitted sensor is scalable and can be facilely and seamlessly integrated into any garment piece. This universal knitted sensor structure could be made with a wide variety of materials for high sensitivity for multidirectional strain/pressure sensing, making it a high-compatibility sensor structure for wearable applications.

Bending Angle Sensor Based on Double-Layer Capacitance Suitable for Human Joint

Goal: To develop bending angle sensors based on double-layer capacitance for monitoring joint angles during cycling exercises. Methods: We develop a bending angle sensor based on double-layer capacitive and conducted three stretching, bending, and cycling tests to evaluate its validity. Results: We demonstrate that the bending angle sensor based on double-layer capacitance minimizes the change in the capacitance difference in the stretching test. The hysteresis and root mean square error (RMSE) compared with the optical motion capture show hysteresis: 8.0% RMSE and 3.1° in the bending test. Moreover, a cycling experiment for human joint angle measurements confirm the changes in accuracy. The RMSEs ranged from 4.7° to 7.0°, even when a human wears leggings fixed with the developed bending-angle sensor in the cycling test. Conclusion: The developed bending angle sensor provides a practical application of the quantitative and observational evaluation tool for knee joint angles.

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

A quantitative evaluation of kinetic parameters, the joint's range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device's positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user's vital signs directly from the body in an accurate and non-invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach's subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post-operative rehabilitation and athletes' training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user's health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.

Development of Low Hysteresis, Linear Weft-Knitted Strain Sensors for Smart Textile Applications

In recent years, knitted strain sensors have been developed that aim to achieve reliable sensing and high wearability, but they are associated with difficulties due to high hysteresis and low gauge factor (GF) values. This study investigated the electromechanical performance of the weft-knitted strain sensors with a systematic approach to achieve reliable knitted sensors. For two elastic yarn types, six conductive yarns with different resistivities, the knitting density as well as the number of conductive courses were considered as variables in the study. We focused on the 1 × 1 rib structure and in the sensing areas co-knit the conductive and elastic yarns and observed that positioning the conductive yarns at the inside was crucial for obtaining sensors with low hysteresis values. We show that using this technique and varying the knitting density, linear sensors with a working range up to 40% with low hysteresis can be obtained. In addition, using this technique and varying the knitting density, linear sensors with a working range up to 40% strain, hysteresis values as low as 0.03, and GFs varying between 0 and 1.19 can be achieved.

Piezoelectric nanogenerator for bio-mechanical strain measurement

Piezoelectric materials have attracted more attention than other materials in the field of textiles. Piezoelectric materials offer advantages as transducers, sensors, and energy-harvesting devices. Commonly, ceramics and quartz are used in such applications. However, polymeric piezoelectric materials have the advantage that they can be converted into any shape and size. In smart textiles, polyvinylidene fluoride (PVDF) and other piezoelectric polymers are used in the form of fibers, filaments, and composites. In this research, PVDF nanofibers were developed and integrated onto a knitted fabric to fabricate a piezoelectric device for human body angle monitoring. Scanning electron microscopy and X-ray diffraction analyses were used to study the morphology and to confirm the beta phase in fibers. The results reveal that the nanofibers made from solutions with high concentration were smooth and defect-free, compared to the fibers obtained from solutions with low concentration, and possess high crystallinity as well. Under high dynamic strain more output voltage is generated than under low dynamic strain. The maximum current density shown by the device is 172.5 nA/cm². The developed piezoelectric nanofiber sensor was then integrated into a knitted fabric through stitching to be used for angle measurement. With increasing bending angle, the output voltage increased. The promising results show that the textile-based piezoelectric sensor developed in this study has a great potential to be used as an angle measuring wearable device for the human body due to its high current density output and flexibility.

A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities

Wearable technology has been developed in recent years to monitor biomechanical variables in less restricted environments and in a more affordable way than optical motion capture systems. This paper proposes the development of a 3D printed knee wearable goniometer that uses a Hall-effect sensor to measure the knee flexion angle, which works with a mobile app that shows the angle in real-time as well as the activity the user is performing (standing, sitting, or walking). Detection of the activity is done through an algorithm that uses the knee angle and angular speeds as inputs. The measurements of the wearable are compared with a commercial goniometer, and, with the Aktos-t system, a commercial motion capture system based on inertial sensors, at three speeds of gait (4.0 km/h, 4.5 km/h, and 5.0 km/h) in nine participants. Specifically, the four differences between maximum and minimum peaks in the gait cycle, starting with heel-strike, were compared by using the mean absolute error, which was between 2.46 and 12.49 on average. In addition, the algorithm was able to predict the three activities during online testing in one participant and detected on average 94.66% of the gait cycles performed by the participants during offline testing.

Monitoring Flexions and Torsions of the Trunk via Gyroscope-Calibrated Capacitive Elastomeric Wearable Sensors

Reliable, easy-to-use, and cost-effective wearable sensors are desirable for continuous measurements of flexions and torsions of the trunk, in order to assess risks and prevent injuries related to body movements in various contexts. Piezo-capacitive stretch sensors, made of dielectric elastomer membranes coated with compliant electrodes, have recently been described as a wearable, lightweight and low-cost technology to monitor body kinematics. An increase of their capacitance upon stretching can be used to sense angular movements. Here, we report on a wearable wireless system that, using two sensing stripes arranged on shoulder straps, can detect flexions and torsions of the trunk, following a simple and fast calibration with a conventional tri-axial gyroscope on board. The piezo-capacitive sensors avoid the errors that would be introduced by continuous sensing with a gyroscope, due to its typical drift. Relative to stereophotogrammetry (non-wearable standard system for motion capture), pure flexions and pure torsions could be detected by the piezo-capacitive sensors with a root mean square error of ~8° and ~12°, respectively, whilst for flexion and torsion components in compound movements, the error was ~13° and ~15°, respectively.

Wearable Detection of Trunk Flexions: Capacitive Elastomeric Sensors Compared to Inertial Sensors

Continuous monitoring of flexions of the trunk via wearable sensors could help various types of workers to reduce risks associated with incorrect postures and movements. Stretchable piezo-capacitive elastomeric sensors based on dielectric elastomers have recently been described as a wearable, lightweight and cost-effective technology to monitor human kinematics. Their stretching causes an increase of capacitance, which can be related to angular movements. Here, we describe a wearable wireless system to detect flexions of the trunk, based on such sensors. In particular, we present: (i) a comparison of different calibration strategies for the capacitive sensors, using either an accelerometer or a gyroscope as an inclinometer; (ii) a comparison of the capacitive sensors’ performance with those of the accelerometer and gyroscope; to that aim, the three types of sensors were evaluated relative to stereophotogrammetry. Compared to the gyroscope, the capacitive sensors showed a higher accuracy. Compared to the accelerometer, their performance was lower when used as quasi-static inclinometers but also higher in case of highly dynamic accelerations. This makes the capacitive sensors attractive as a complementary, rather than alternative, technology to inertial sensors.

A 3D-Printed Soft Fingertip Sensor for Providing Information about Normal and Shear Components of Interaction Forces

Sensing of the interaction forces at fingertips is of great value in assessment and rehabilitation therapy. Current force sensors are not compliant to the fingertip tissue and result in loss of touch sensation of the user. This work shows the development and characterization of a flexible fully-3D-printed piezoresistive shear and normal force sensor that uses the mechanical deformation of the finger tissue. Two prototypes of the sensing structure are evaluated using a finite element model and a measurement setup that applies normal and shear forces up to 10 N on a fingertip phantom placed inside the sensing structure, which is fixed to prevent slippage. Furthermore, the relation between strain (rate) and resistance of the conductive TPU, used for the strain gauges, is characterized. The applied normal and shear force components of the 3D-printed sensing structure can be partly separated. FEM analysis showed that the output of the sensor is largely related to the sensor geometry and location of the strain gauges. Furthermore, the conductive TPU that was used has a negative gauge factor for the strain range used in this study and might cause non-linear behaviors in the sensor output.

A Wearable Sensor-Based Platform for Surgeon Posture Monitoring: A Tool to Prevent Musculoskeletal Disorders

Surgeons are workers that are particularly prone to the development of musculoskeletal disorders. Recent advances in surgical interventions, such as laparoscopic procedures, have caused a worsening of the scenario, given the harmful static postures that have to be kept for long periods. In this paper, we present a sensor-based platform specifically aimed at monitoring the posture during actual surgical operations. The proposed system adopts a limited number of Inertial Measurement Units (IMUs) to obtain information about spine and neck angles across time. Such a system merges the reliability of sensor-based approaches and the validity of state-of-the-art scoring procedure, such as RULA. Specifically, three IMUs are used to estimate the flexion, lateral bending, and twisting angles of spine and neck. An ergonomic risk index is thus estimated in a time varying fashion borrowing relevant features from the RULA scoring system. The detailed functioning of the proposed systems is introduced, and the assessment results related to a real surgical procedure, consisting of a laparoscopy and mini-laparotomy sections, are shown and discussed. In the exemplary case study introduced, the surgeon kept a high score, indicating the need for an intervention on the working procedures, for a large time fraction. The system allows separately analyzing the contribution of spine and neck, also specifying the angle configuration. It is shown how the proposed approach can provide further information, as related to dynamical analysis, which could be used to enlarge the features taken into account by currently available approaches for ergonomic risk assessment. The proposed system could be adopted both for training purposes, as well as for alerting surgeons during actual surgical operations.

Performance Evaluation of Knitted and Stitched Textile Strain Sensors

By embedding conductive yarns in, or onto, knitted textile fabrics, simple but robust stretch sensor garments can be manufactured. In that way resistance based sensors can be fully integrated in textiles without compromising wearing comfort, stretchiness, washability, and ease of use in daily life. The many studies on such textile strain sensors that have been published in recent years show that these sensors work in principle, but closer inspection reveals that many of them still have severe practical limitations like a too narrow working range, lack of sensitivity, and undesired time-dependent and hysteresis effects. For those that intend to use this technology it is difficult to determine which manufacturing parameters, shape, stitch type, and materials to apply to realize a functional sensor for a given application. This paper therefore aims to serve as a guideline for the fashion designers, electronic engineers, textile researchers, movement scientists, and human–computer interaction specialists planning to create stretch sensor garments. The paper is limited to textile based sensors that can be constructed using commercially available conductive yarns and existing knitting and embroidery equipment. Within this subtopic, relevant literature is discussed, and a detailed quantitative comparison is provided focusing on sensor characteristics like the gauge factor, working range, and hysteresis.

Recent Trends, Construction and Applications of Smart Textiles and Clothing for Monitoring of Health Activity: A Comprehensive Multidisciplinary Review

In the area of biomedical signal monitoring, wearable electronics represents a dynamically growing field with a significant impact on the market of commercial products of biomedical signal monitoring and acquisition, as well as consumer electronic for vital functions monitoring. Since the electrodes are perceived as one of the most important part of the biomedical signal monitoring, they have been one of the most frequent subjects in the research community. Electronic textile (e-textile), also called smart textile represents a modern trend in the wearable electronics, integrating of functional materials with common clothing with the goal to realize the devices, which include sensors, antennas, energy harvesters and advanced textiles for self-cooling and heating. The area of textile electrodes and e-textile is perceived as a multidisciplinary field, integrating material engineering, chemistry, and biomedical engineering. In this review, we provide a comprehensive view on this area. This multidisciplinary review integrates the e-textile characteristics, materials and manufacturing of the textile electrodes, noise influence on the e-textiles performance, and mainly applications of the textile electrodes for biomedical signal monitoring and acquisition, including pressure sensors, electrocardiography, electromyography, electroencephalography and electrooculography monitoring.

Design and Initial Testing of an Affordable and Accessible Smart Compression Garment to Measure Physical Activity Using Conductive Paint Stretch Sensors

Motion capture and the measurement of physical activity are common practices in the fields of physical therapy, sports medicine, biomechanics, and kinesiology. The data collected by these systems can be very important to understand how someone is recovering or how effective various assistive devices may be. Traditional motion capture systems are very expensive and only allow for data collection to be performed in a lab environment. In our previous research, we have tested the validity of a novel stitched stretch sensor using conductive thread. This paper furthers that research by validating a smart compression garment with integrated conductive paint stretch sensors to measure movement. These sensors are very inexpensive to fabricate and, when paired with an open-sourced wireless microcontroller, can enable a more affordable, accessible, and comfortable form of motion capture. A wearable garment like the one tested in this study could allow us to understand how meaningful, functional activities are performed in a natural setting.

Wearable Sensors for Monitoring Human Motion: A Review on Mechanisms, Materials, and Challenges

The emergence of flexible wearable electronics as a new platform for accurate, unobtrusive, user-friendly, and longitudinal sensing has opened new horizons for personalized assistive tools for monitoring human locomotion and physiological signals. Herein, we survey recent advances in methodologies and materials involved in unobtrusively sensing a medium to large range of applied pressures and motions, such as those encountered in large-scale body and limb movements or posture detection. We discuss three commonly used methodologies in human gait studies: inertial, optical, and angular sensors. Next, we survey the various kinds of electromechanical devices (piezoresistive, piezoelectric, capacitive, triboelectric, and transistive) that are incorporated into these sensor systems; define the key metrics used to quantitate, compare, and optimize the efficiency of these technologies; and highlight state-of-the-art examples. In the end, we provide the readers with guidelines and perspectives to address the current challenges of the field.

Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions.

A review of e-textiles in neurological rehabilitation: How close are we?

Textiles able to perform electronic functions are known as e-textiles, and are poised to revolutionise the manner in which rehabilitation and assistive technology is provided. With numerous reports in mainstream media of the possibilities and promise of e-textiles it is timely to review research work in this area related to neurological rehabilitation.This paper provides a review based on a systematic search conducted using EBSCO- Health, Scopus, AMED, PEDro and ProQuest databases, complemented by articles sourced from reference lists. Articles were included if the e-textile technology described had the potential for use in neurological rehabilitation and had been trialled on human participants. A total of 108 records were identified and screened, with 20 meeting the broad review inclusion criteria. Nineteen user trials of healthy people and one pilot study with stroke participants have been reported.The review identifies two areas of research focus; motion sensing, and the measurement of, or stimulation of, muscle activity. In terms of motion sensing, E-textiles appear able to reliably measure gross movement and whether an individual has achieved a predetermined movement pattern. However, the technology still remains somewhat cumbersome and lacking in resolution at present. The measurement of muscle activity and the provision of functional electrical stimulation via e-textiles is in the initial stages of development but shows potential for e-textile expansion into assistive technologies.The review identified a lack of high quality clinical evidence and, in some cases, a lack of practicality for clinical application. These issues may be overcome by engagement of clinicians in e-textile research and using their expertise to develop products that augment and enhance neurological rehabilitation practice.

A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition

Achieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness.

A bi-articular model for scapular-humeral rhythm reconstruction through data from wearable sensors

BACKGROUND

Patient-specific performance assessment of arm movements in daily life activities is fundamental for neurological rehabilitation therapy. In most applications, the shoulder movement is simplified through a socket-ball joint, neglecting the movement of the scapular-thoracic complex. This may lead to significant errors. We propose an innovative bi-articular model of the human shoulder for estimating the position of the hand in relation to the sternum. The model takes into account both the scapular-toracic and gleno-humeral movements and their ratio governed by the scapular-humeral rhythm, fusing the information of inertial and textile-based strain sensors.

METHOD

To feed the reconstruction algorithm based on the bi-articular model, an ad-hoc sensing shirt was developed. The shirt was equipped with two inertial measurement units (IMUs) and an integrated textile strain sensor. We built the bi-articular model starting from the data obtained in two planar movements (arm abduction and flexion in the sagittal plane) and analysing the error between the reference data - measured through an optical reference system - and the socket-ball approximation of the shoulder. The 3D model was developed by extending the behaviour of the kinematic chain revealed in the planar trajectories through a parameter identification that takes into account the body structure of the subject.

RESULT

The bi-articular model was evaluated in five subjects in comparison with the optical reference system. The errors were computed in terms of distance between the reference position of the trochlea (end-effector) and the correspondent model estimation. The introduced method remarkably improved the estimation of the position of the trochlea (and consequently the estimation of the hand position during reaching activities) reducing position errors from 11.5 cm to 1.8 cm.

CONCLUSION

Thanks to the developed bi-articular model, we demonstrated a reliable estimation of the upper arm kinematics with a minimal sensing system suitable for daily life monitoring of recovery.

Wearable Textile Platform for Assessing Stroke Patient Treatment in Daily Life Conditions

Monitoring physical activities during post-stroke rehabilitation in daily life may help physicians to optimize and tailor the training program for patients. The European research project INTERACTION (FP7-ICT-2011-7-287351) evaluated motor capabilities in stroke patients during the recovery treatment period. We developed wearable sensing platform based on the sensor fusion among inertial, knitted piezoresistive sensors and textile EMG electrodes. The device was conceived in modular form and consists of a separate shirt, trousers, glove, and shoe. Thanks to the novel fusion approach it has been possible to develop a model for the shoulder taking into account the scapulo-thoracic joint of the scapular girdle, considerably improving the estimation of the hand position in reaching activities. In order to minimize the sensor set used to monitor gait, a single inertial sensor fused with a textile goniometer proved to reconstruct the orientation of all the body segments of the leg. Finally, the sensing glove, endowed with three textile goniometers and three force sensors showed good capabilities in the reconstruction of grasping activities and evaluating the interaction of the hand with the environment, according to the project specifications. This paper reports on the design and the technical evaluation of the performance of the sensing platform, tested on healthy subjects.

Wearable Goniometer and Accelerometer Sensory Fusion for Knee Joint Angle Measurement in Daily Life

Human motion analysis is crucial for a wide range of applications and disciplines. The development and validation of low cost and unobtrusive sensing systems for ambulatory motion detection is still an open issue. Inertial measurement systems and e-textile sensors are emerging as potential technologies for daily life situations. We developed and conducted a preliminary evaluation of an innovative sensing concept that combines e-textiles and tri-axial accelerometers for ambulatory human motion analysis. Our sensory fusion method is based on a Kalman filter technique and combines the outputs of textile electrogoniometers and accelerometers without making any assumptions regarding the initial accelerometer position and orientation. We used our technique to measure the flexion-extension angle of the knee in different motion tasks (monopodalic flexions and walking at different velocities). The estimation technique was benchmarked against a commercial measurement system based on inertial measurement units and performed reliably for all of the various tasks (mean and standard deviation of the root mean square error of 1:96 and 0:96, respectively). In addition, the method showed a notable improvement in angular estimation compared to the estimation derived by the textile goniometer and accelerometer considered separately. In future work, we will extend this method to more complex and multi-degree of freedom joints.

Daily-life monitoring of stroke survivors motor performance: the INTERACTION sensing system

The objective of the INTERACTION Eu project is to develop and validate an unobtrusive and modular system for monitoring daily life activities, physical interactions with the environment and for training upper and lower extremity motor function in stroke subjects. This paper describes the development and preliminary testing of the project sensing platform made of sensing shirt, trousers, gloves and shoes. Modular prototypes were designed and built considering the minimal set of inertial, force and textile sensors that may enable an efficient monitoring of stroke patients. The single sensing elements are described and the results of their preliminary lab-level testing are reported.

Inexpensive Vision-Based System for the Direct Measurement of Ankle Stiffness During Quiet Standing

We created a sensor-fusion suite for the acquisition of biometric information that can be used for the estimation of human control strategy in a variety of everyday tasks. This work focuses on the experimental validation of the integrated motion capture subsystem based on raster images. Understanding human control strategies utilized in everyday activity requires measurement of several variables that can be grouped as kinematic, dynamic, and biological-feedback variables. Hence, there is a strong need for the acquisition, analysis, and synchronization of the information measured by a variety of transducers. Our system was able to capture the complex dynamics of a flexible robot by means of two inexpensive web cameras without compromising accuracy. After validating the vision system by means of the robotic device, a direct measure of the center of gravity (COG) position during the recovery from a fall was performed on two groups of human subjects separated by age. The instrumental setup was used to estimate how ankle operational stiffness changes as function of age. The results indicate a statistical increase of stiffness for the older group.