Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Comparison of the Proprioceptive and Motion Reduction Effects of Shoulder Braces in Individuals With and Without Anterior Shoulder Dislocations: A Pilot Study

BACKGROUND AND PURPOSE: In individuals with a history of anterior shoulderdislocation, research has shown that proprioception can become impaired. Conservativeintervention often includes the use of a shoulder brace for activities. There is currently noresearch that compares the efficacy of shoulder braces in limiting range of motion (ROM) and providing proprioceptive feedback of the shoulder. The purpose of this pilot studywas to investigate the effects of various shoulder braces on glenohumeral ROM andproprioception in individuals with a history of anterior shoulder dislocation. Subjectswithout a history of dislocation were also recruited to assess the feasibility of themethodology utilized in this study. METHODS: Eight subjects’ maximal ROM and proprioception were tested in threeconditions: 1) no brace; 2) Duke Wyre; and 3) Sully. Kinematic data for bothproprioception and ROM was collected using an electromagnetic 3-dimensional motioncapture system. Humeral motions tested were: 1) abduction; 2) maximal external rotationat 90° of abduction; and 3) combined motion. Proprioception was tested using activereplication of three standardized external rotation (ER) positions of the shoulder.Outcome measures included motion restriction compared to the no brace condition andthe relative error in active replication at each of the three ER positions. RESULTS: ANOVA’s were run for each ROM and proprioception condition and ifsignificant, post-hoc, independent t-tests were performed. Significance for all tests wasset at 0.05. Statistically significant findings between all brace conditions were found withglenohumeral ER and abduction. Significant differences in combined ranges of motionwere found between the no-brace condition and braced conditions. Proprioceptive testingrevealed statistically significant findings between the no-brace condition and the Sully,and between the Sully and Duke Wyre at 10 degrees of ER. Ten degrees short ofmaximal ER revealed statistical significance between the Sully and other two conditions. CONCLUSION: Both the Duke Wyre and the Sully shoulder braces limit glenohumeralROM. The Sully increased shoulder proprioception in positions vulnerable to dislocation.The study design and methods performed will enable future research to expand upon thedata gathered in order to benefit both the clinician and athletic populations

Device to Detect Sitting to Standing Transitions

ME450 Capstone Design and Manufacturing Experience: Fall 2020Device to Detect Sitting to Standing TransitionsDr. Lauro Ojeda, Dr. Lauro Ojeda (UM-ME)http://deepblue.lib.umich.edu/bitstream/2027.42/164439/1/Device_to_Detect_Sitting_to_Standing_Transitions.pd

Fiber Bragg Gratings as e-Health Enablers: An Overview for Gait Analysis Applications

Nowadays, the fast advances in sensing technologies and ubiquitous wireless networking are reflected in medical practice. It provides new healthcare advantages under the scope of e-Health applications, enhancing life quality of citizens. The increase of life expectancy of current population comes with its challenges and growing health risks, which include locomotive problems. Such impairments and its rehabilitation require a close monitoring and continuous evaluation, which add financial burdens on an already overloaded healthcare system. Analysis of body movements and gait pattern can help in the rehabilitation of such problems. These monitoring systems should be noninvasive and comfortable, in order to not jeopardize the mobility and the day-to-day activities of citizens. The use of fiber Bragg gratings (FBGs) as e-Health enablers has presented itself as a new topic to be investigated, exploiting the FBGs’ advantages over its electronic counterparts. Although gait analysis has been widely assessed, the use of FBGs in biomechanics and rehabilitation is recent, with a wide field of applications. This chapter provides a review of the application of FBGs for gait analysis monitoring, namely its use in topics such as the monitoring of plantar pressure, angle, and torsion and its integration in rehabilitation exoskeletons and for prosthetic control

A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks

Foot and Ankle Impairments Affecting Mobility in Stroke

Introduction: Altered foot characteristics are common in people with stroke, with a third presenting with abnormal foot posture which is associated with ambulatory difficulties. Understanding the relationship between measures of foot and ankle impairment and their association with mobility and balance outcomes is therefore important; however, poor clinimetric properties of foot and ankle measures after stroke precludes evaluation of these relationships. Therefore, this research, undertaken as part of a multicentred research project, had the following aims: Study 1: To evaluate the clinimetric properties (feasibility, test–retest reliability, and clinical relevance) of measures of foot and ankle impairments, for application in people with stroke. Study 2: To examine how these measures differ between people with stroke and normal controls; and whether they are associated with mobility and balance outcomes. Methods: In Study 1, community-dwelling people with stroke, able to walk 10 m (metres), attended two testing sessions to evaluate the clinimetric properties of different foot and ankle measures. These included: static foot posture and dynamic foot loading (peak plantar pressure, PPP, contact area, CA and centre of pressure, CP) using a plantar pressure mat; isometric muscle strength using a hand-held dynamometer (HHD); peak ankle and hallux dorsiflexion and stiffness using bespoke rigs; and ankle plantarflexion spasticity using the Tardieu scale. Statistical analysis used intraclass correlation coefficients (ICCs₍₃,₁₎), standard error of measurement (SEM) and Bland–Altman plots. In Study 2, measures identified as reliable from Study 1 were incorporated in a cross-sectional study design. Participants were recruited from acute and community neurological services in East London and North Devon. Statistical analysis tested the differences between groups and between affected limbs in people with stroke. Impairment measures were evaluated using multivariate regression analysis for their association with functional outcomes: walking speed (over 10 m); Timed Up and Go (TUAG), Forward Functional Reach Test (FFRT) and presence of falls (> 1 in the last 3 months). Results: In Study 1, 21 people with stroke tested the measures. These were found to be feasible and easy to administer, although loss of data (up to 33%) was observed. All measures had moderate to excellent test–retest reliability (coefficients 0.50‒0.98), except ankle plantarflexion stiffness (ICCs₍₃,₁₎ = 0.00‒0.11). In Study 2, there were significant differences in all measures between people with stroke (n = 180) and controls (n = 46), apart from static foot posture (p = 0.670), toe deformity (p = 0.782) and peak hallux dorsiflexion (p = 0.320). Between limb differences were identified for all measures except foot posture (p = 0.489) and foot CA (p > 0.05). Multicollinearity analysis found 10 measures appropriate for multivariate regression which identified the following R² and variance explained: 59% walking speed (R² = 0.543); 49% TUAG (R² = 0.435); 36% FFRT (R² = 0.285) and 26% for Falls Presence. Conclusion: The study demonstrated that seven foot and ankle measures of impairment after stroke were clinically feasible, reliable and associated with mobility and balance outcomes. The measures were ankle and foot isometric muscle strength, sway velocity, PPP (RFT and FFT), CA (MFT and FFT) and peak ankle dorsiflexion. These measures can now be incorporated into research to examine methods to improve the treatment of foot and ankle after stroke

mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder

In this paper we propose mmFall - a novel fall detection system, which comprises of (i) the emerging millimeter-wave (mmWave) radar sensor to collect the human body's point cloud along with the body centroid, and (ii) a variational recurrent autoencoder (VRAE) to compute the anomaly level of the body motion based on the acquired point cloud. A fall is claimed to have occurred when the spike in anomaly level and the drop in centroid height occur simultaneously. The mmWave radar sensor provides several advantages, such as privacycompliance and high-sensitivity to motion, over the traditional sensing modalities. However, (i) randomness in radar point cloud data and (ii) difficulties in fall collection/labeling in the traditional supervised fall detection approaches are the two main challenges. To overcome the randomness in radar data, the proposed VRAE uses variational inference, a probabilistic approach rather than the traditional deterministic approach, to infer the posterior probability of the body's latent motion state at each frame, followed by a recurrent neural network (RNN) to learn the temporal features of the motion over multiple frames. Moreover, to circumvent the difficulties in fall data collection/labeling, the VRAE is built upon an autoencoder architecture in a semi-supervised approach, and trained on only normal activities of daily living (ADL) such that in the inference stage the VRAE will generate a spike in the anomaly level once an abnormal motion, such as fall, occurs. During the experiment, we implemented the VRAE along with two other baselines, and tested on the dataset collected in an apartment. The receiver operating characteristic (ROC) curve indicates that our proposed model outperforms the other two baselines, and achieves 98% detection out of 50 falls at the expense of just 2 false alarms.Comment: Preprint versio

Inertial sensors signal processing methods for gait analysis of patients with impaired gait patterns

Analiza hoda je postala široko rasprostranjen klinički alat koji se koristi za objektivnu evaluaciju obrasca hoda, efekata hirurških intervencija, oporavka ili efekata terapije. Sve veći broj kliničara bira pogodne tretmane za lečenje pacijenata na osnovu informacija o kinematici i kinetici hoda. Procena i kvantifikacija parametara hoda je važan zahtev u oblasti ortopedije i rehabilitacije, ali takođe i u sportu, rekreaciji i posebno u razvoju tehnologija za ljude u procesu starenja. U cilju objektivne procene obrasca hoda, razvijen je bežični senzorski sistem čije su senzorske jedinice bežične, malih dimenzija i jednostavno se montiraju na segmente nogu subjekta čiji se hoda analizira. Senzorske jedinice podržavaju 3D inercijalne senzore (senzore ubrzanja i ugaonih brzina, tj. akcelerometre i žiroskope), kao i senzore sile. Osnovni cilj istraživanja je doprinos metodologiji za obradu podataka sa inercijalnih senzora i razvoj novih metoda obrade signala sa inercijalnih senzora u procesu određivanja kinematičkih veličina koje su uobičajene u analizi hoda (uglovi u zglobovima, brzina kretanja, dužina koraka). Ova metodologija je od posebne važnosti za objektivnu procenu nivoa motornog deficita, progresa bolesti i efikasnosti terapija, kao i efikasnosti primenjene motorne kontrole (prilikom funkcionalne električne stimulacije). U toku istraživanja razvijeno je nekoliko metoda za računanje uglova segmenata nogu ili zglobova, u zavisnosti od senzorske konfiguracije i složenosti algoritma. U disertaciji su odvojeno prikazani slučajevi u kojima je neophodno posmatrati kretanje u prostoru (3D analiza) i mnogo češći slučaj kad se kinematika može redukovati na sagitalnu ravan (2D analiza). Algoritmi uključuju i kalibraciju senzora, eliminaciju viii drifta, rekonstrukciju trajektorije i izračunavanje niza drugih relevantnih podataka koji karakterišu obrazac hoda. Dobijeni rezultati su poređeni sa postojećim sistemima za analizu hoda koji su validirani za kliničke primene. (sistemi sa kamerama, goniometri, enkoderi)...Gait analysis has become a widely used clinical tool which provides objective evaluation of the gait pattern, the effects of surgical interventions, recovery or therapy progress, and more and more clinicians are choosing therapy treatments based on gait kinematics and kinetics. Measuring gait parameters is an important requirement in the orthopedic and rehabilitation fields, but also in sports and fitness, and development of technologies for elderly population. In order to provide objective evaluation of the gait pattern, we have developed sensor system with light and small wireless sensor units, which can be easily mounted on body. These sensor units comprise 3-D inertial sensors (accelerometers and gyroscopes) and force sensing resistors, and our recommended setup includes one sensor unit per each segment of both legs. The main goal of this research is contribution to the methodology for processing of signals from inertial sensors (accelerometer pairs, or accelerometer and gyroscope sensor units). By using signal processing algorithms developed for this research, inertial sensors allow objective assessment of the quality of the gait pattern. This methodology is especially important for assessment of the motor deficit, progress of the disease and therapy effectiveness, and effectiveness of performed motor control (functional electrical stimulation). We have developed several methods for estimation of leg segment angles and joint angles, which differ in sensor configuration and algorithm complexity. Methods based only on accelerometers offer reliable angle estimations, which are limited to sagittal plane analysis, while the method using accelerometers and gyroscopes allows 3- D analysis. All this algorithms include sensor calibration, drift minimization, trajectory x reconstruction and calculation of numerous other parameters relevant to gait pattern analysis. The obtained results were compared with other commercial systems which are validated for clinical applications (camera systems, goniometers, encoders)..