ウェアラブルセンサシステムを用いた身体パラメータ推定に関する研究

Wearable sensors, in particular inertial measurement units (IMUs) allow the objective, valid, discriminative and responsive assessment of physical function during functional tests such as gait, stair climbing or sit-to-stand.Applied to various body segments, precise capture of time-to-task achievement, spatiotemporal gait and kinematic parameters of demanding tests or specific to an affected limb are the most used measures.In activity monitoring (AM), accelerometry has mainly been used to derive energy expenditure or general health related parameters such as total step counts.In orthopaedics and the elderly, counting specific events such as stairs or high intensity activities were clinimetrically most powerful; as were qualitative parameters at the ‘micro-level’ of activity such as step frequency or sit-stand duration.Low cost and ease of use allow routine clinical application but with many options for sensors, algorithms, test and parameter definitions, choice and comparability remain difficult, calling for consensus or standardisation.Cite this article: Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev 2016;1:112–120. DOI: 10.1302/2058-5241.1.160022.

The goal of this paper is to localize a resident in indoor environments by using motion information from distributed environmental sensors and body activity information from wearable sensors. The passive infrared sensor nodes distributed in a home provide binary information about human motion in their field of views, while the wearable inertial measurement unit sensor node collects motion data that can be used in body activity recognition, walking velocity, and heading estimation. Basic human activities such as sitting, sleeping, standing, and walking are recognized. We proposed a particle filter-based sensor fusion algorithm that takes advantage of the human location/activity correlation in indoor environments to increase the localization accuracy. Experiments were conducted in a mock apartment testbed. We used the ground truth data obtained from a motion capture system to evaluate the results. Note to Practitioners —This paper is motivated by a goal to realize various home automation applications through human localization. For example, with the knowledge of human location, lighting, and air conditioning systems can be more efficiently managed, or a home service robot can provide better service to the resident. There currently lacks an accurate human localization method in indoor environments. The theoretical framework proposed in this paper aims to combine simple environmental sensors (passive infrared sensors) and a single wearable motion sensor to accurately track human locations. This method reduces the intrusiveness to the human, while keeping the cost of the infrastructure low. The theoretical framework has been tested and evaluated in a mock apartment environment as a proof of concept. For deployment into real homes, we need to conduct more realistic tests and thorough evaluation of the system. It is also necessary to reduce the size and weight of the wearable motion sensor and simplify the installation of the environmental sensors.

Dynamic Thumb Circumduction Measured With a Wearable Motion Sensor: A Prospective Comparison of Patients With Basal Joint Arthritis to Controls

BackgroundTotal knee arthroplasty (TKA) is a common treatment for severe knee osteoarthritis. Medial-pivot TKA systems (MP-TKA) are theoretically better than posterior-stabilized TKA systems (PS-TKA) in improving static and dynamic balance of patients although it is difficult to objectively quantify these balance parameters in a clinical setting.Therefore, this pilot study aimed to evaluate the feasibility of using wearable devices in a clinical setting to examine whether people with MP-TKA have better postoperative outcomes than PS-TKA, and their balance control is more akin to age-matched asymptomatic controls.MethodsThe current cross-sectional pilot study recruited 57 participants with 2 different prosthesis designs (20 PS-TKA, 18 MP-TKA) and 19 asymptomatic controls. At 1-year post-TKA, pain, knee stiffness, and physical function were assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Static balance, mobility, and gait stability of the participants were evaluated based on data collected from wearable motion sensors during the near tandem stance, timed-up-and-go, and 6-min walk tests.ResultsCompared to asymptomatic controls, both TKA groups reported significantly more pain and stiffness and demonstrated reduced functional mobility, increased stride-time-variability, and impaired balance. After Bonferroni adjustment, no significant differences in pain, balance, and mobility performance were observed between PS-TKA and MP-TKA participants 1 year after surgery. However, there was a trend for increased anteroposterior sway of the lumbar and head regions in the MP-TKA participants when undertaking the near tandem stance test. The wearable motion sensors were easy to use without any adverse effects.ConclusionsIt is feasible to use wearable motion sensors in a clinical setting to compare balance and mobility performance of patients with different TKA prothesis designs. Since this was a pilot study and no definite conclusions could be drawn, future clinical trials should determine the impacts of different TKA prosthesis designs on post-operative outcomes over a longer follow-up period.

IoT-enabled medical advances shaping the future of orthopaedic surgery and rehabilitation.

Measurement of scratching motion is important as an objective index of itching for quantifying the severity of skin diseases and treatment effects. In this paper, we developed a scratching motion measurement sensor system combining a microphone sensor, a 3-axis acceleration sensor, and a fingertip force measurement sensor using a strain gauge to measure not only the number of scratching motion but also the strength and speed of the scratching motion. By using the sensor system, we measured the scratching motion against an artificial skin made of polyurethane resin. In the experiments, the fingertip position and contact force of finger to artificial skin were measured by a motion capture system and a load cell as comparative data, respectively. Regarding the contact force, it was confirmed that the maximum output of finger force sensor related to the maximum load cell output. And it was suggested that fingertip speed can be estimated from the accelerometer output. From the result, it was found that the effectiveness of the developed sensor system.

Fluctuations in response to levodopa in Parkinson's disease (PD) are difficult to treat as tools to monitor temporal patterns of symptoms are hampered by several challenges. The objective was to use wearable sensors to quantify the dose response of tremor, bradykinesia, and dyskinesia in individuals with PD. Thirteen individuals with PD and fluctuating motor benefit were instrumented with wrist and ankle motion sensors and recorded by video. Kinematic data were recorded as subjects completed a series of activities in a simulated home environment through transition from off to on medication. Subjects were evaluated using the unified Parkinson disease rating scale motor exam (UPDRS-III) at the start and end of data collection. Algorithms were applied to the kinematic data to score tremor, bradykinesia, and dyskinesia. A blinded clinician rated severity observed on video. Accuracy of algorithms was evaluated by comparing scores with clinician ratings using a receiver operating characteristic (ROC) analysis. Algorithm scores for tremor, bradykinesia, and dyskinesia agreed with clinician ratings of video recordings (ROC area > 0.8). Summary metrics extracted from time intervals before and after taking medication provided quantitative measures of therapeutic response (p < 0.01). Radar charts provided intuitive visualization, with graphical features correlated with UPDRS-III scores (R = 0.81). A system with wrist and ankle motion sensors can provide accurate measures of tremor, bradykinesia, and dyskinesia as patients complete routine activities. This technology could provide insight on motor fluctuations in the context of daily life to guide clinical management and aid in development of new therapies.

The hypothesis that the effectiveness of neurosurgical procedures in Parkinson's disease (PD) would be related to connectivity dysfunctions between the site of the stimulation and other brain regions is growing. This study aimed to assess resting-state functional connectivity between thalamic ventral intermediate nucleus (Vim) and the rest of the brain before and after thalamotomy in PD. A 76-year-old right-handed woman with refractory tremor-dominant PD was selected as a candidate for left Vim radiosurgery thalamotomy. Clinicaland motion sensor evaluation and brainresting-state functionalMRI (rs-fMRI) were carried out before treatment and 3, 6, and 12months later. Targeted Vim was selected as region of interest and a seed-based rs-fMRI analysis was performed in the patient and ten age- and sex-matched controls at baseline and over time. Furthermore, a correlation analysis between functional connectivity and tremor data was carried out. Both clinical and motion sensor measurements showed a progressive tremor improvement over time on right side after radiosurgery. In the patient, seed-based analysis showed a significantly increased functional connectivity between targeted Vim and ipsilateral visual areas relative to controls before treatment. Over 1year, a normalization of aberrant pre-therapeutic functional connectivity between Vim and visual areas was obtained. At correlation analysis, the reduction of tremor metrics over time, assessed by clinicalevaluation and wearable motion sensors, was related to the reduction of the left Vim-left visual cortex functional connectivity. Our findings support the evidence that fMRI was able to detect targeted Vim connectivity and its changes over time after thalamotomy.

Wearable inertial sensors have demonstrated their potential to screen for various neuropathies and neurological disorders. Most such research has been based on classification algorithms that differentiate the control group from the pathological group, using biomarkers extracted from wearable data as predictors. However, such methods often lack quantitative evaluation of how much information provided by the wearable biomarkers contributes to the overall prediction. Despite promising results from internal cross validation, their utility in clinical practice remains unclear. In this paper, we highlight in a case study - early screening for diabetic peripheral neuropathy (DPN) - evaluation methods for quantifying the contribution of wearable inertial sensors. Using a quick-to-deploy wearable sensor system, we collected 106 in-hospital diabetic patients' gait data and developed logistic regression models to predict the risk of a diabetic patient having DPN. Adopting various metrics, we evaluated the discriminative information added by gait biomarkers and how much it improved screening. The results show that the proposed wearable system added useful information significantly $(\mathbf{p} to the existing clinical standards, and boosted the C-index significantly $(\mathbf{p from 0.75 to 0.84, surpassing the current survey-based screening methods used in clinics.

We present a novel concept for an audience voting system for judged sports events. It is based on wireless wearable motion sensors utilizing the natural behaviour of sports spectators -clapping and cheering. This paper describes initial results from a user study that guided our design process. We further present two preliminary prototypes focusing on hardware and design feasibility respectively. Results confirm our assumption that the system does not provide objective results, but has the potential to increase the spectator experience by giving audience members the feeling of being part of the voting process.

A new treatment, deep brain stimulation (DBS), is available for the management Parkinson’s disease. However, the assessment of its efficacy is limited by the inadequacies of current methods. The present study was performed to develop and validate a portable device capable of objectively measuring the performance and therapeutic efficiency of Parkinson’s disease by walking performance. The developed wearable motion sensor with accelerometer and gyro sensor was used to measure walking performance under DBS-off, DBS-on, and medication conditions. The root mean square (RMS) and coefficient of variations (CV) of acceleration signals were compared between controls and patients with medication. The results indicated significantly improved walking balance after medication. DBS-on improved performance but the effect was not statistically significant.

Activity recognition systems have demonstrated potential in a broad range of applications. A crucial aspect of creating large scale human activity sensing corpus is to develop algorithms that perform activity recognition in a way that users are not limited to wear sensors on predefined locations on the body. Therefore, effective on-body sensor localization algorithms are needed to detect the location of wearable sensors automatically and in real-time. However, power optimization is a major concern in the design of these systems. Frequent need to charge multiple sensor nodes imposes much burden on the end-users. In this paper, we propose a novel signal processing approach that leverages feature selection algorithms to minimize power consumption of node localization. With the real data collected using wearable motion sensors, we demonstrate that the proposed approach achieves an energy saving that ranges from 88% to 99.59% while obtaining an accuracy performance between 73.15% and 99.85%.

Analysis of construction trade worker body motions using a wearable and wireless motion sensor network

This paper presents a study on quantitative dynamics analysis of human lower limb using developed wearable sensor systems that can measure reaction force and detect the following gait phases: initial contact, loading response, mid stance, terminal stance, pre-swing, initial swing, mid swing and terminal swing. Since conventional camera-based motion analysis system and reaction force plate system require costly devices, vast space as well as time-consuming calibration experiments, the wearable sensor-based system is much cheaper. Gyroscopes and two-axis accelerometers are incorporated in this wearable sensor system. The former are attached on the surface of the foot, shank and thigh to measure the angular velocity of each segment, and the latter are used to measure inclination of the attached leg segment (shank) in every single human motion cycle for recalibration. Ground reaction forces during human walking are synchronously measured using a wearable force sensor integrated in a shoes mechanism. Finally, experiment has been performed to compare the measurement results from the wearable sensor system with the data obtained from an optical motion analysis system and a force plate. The results showed that the measurement of human lower limb orientations and reaction forces for human dynamics analysis could be reliably implemented using the wearable sensor systems.

On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, artificial intelligence (AI) is fundamentally transforming the way healthcare is provided. Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. Initial development of AI algorithms for disease monitoring, technical achievements, and constant enhancements driven by early user feedback are addressed in the discussion section. To ascertain the platform’s trustworthiness and data security, it also points towards risk analysis and mitigation approaches. The NeuroPredict platform’s capability for achieving AI-driven smart healthcare solutions is highlighted, even though it is currently in the development stage. Subsequent research is expected to focus on boosting data integration, expanding AI models, and providing regulatory compliance for clinical application. The current results are based on incremental laboratory tests using simulated user roles, with no clinical patient data involved so far. This study reports an experimental technology evaluation of modular components of the NeuroPredict platform, integrating multimodal sensors and machine learning pipelines in a laboratory-based setting, with future co-design and clinical validation foreseen for a later project phase.