Using wearable sensors to characterize gait after spinal cord injury: evaluation of test-retest reliability and construct validity

Reliability of the Mini-BESTest and Brief-BESTest for assessing patients with incomplete spinal cord injury

STUDY DESIGN

Single institution observational study.

OBJECTIVES

To investigate the ceiling and floor effects of the Berg Balance Scale (BBS), Mini-Balance Evaluation Systems Test (BESTest), and Brief-BESTest, as well as to determine the intra- and inter-rater reliabilities and minimal detectable change (MDC) of the Mini-BESTest and Brief-BESTest in patients with acute and subacute incomplete cervical spinal cord injury (SCI) classified as AIS D.

SETTING

Advanced critical care center of our university hospital.

METHODS

Twenty patients with incomplete cervical SCI who could stand without assistance were recruited. The floor and ceiling effects were evaluated by plotting histograms from the distribution of scores on the BBS, Mini-BESTest and Brief-BESTest, and calculating skewness. The Mini-BESTest and Brief-BESTest were evaluated and videotaped simultaneously, and intra- and inter-rater reliabilities were assessed. The MDC was also calculated.

RESULTS

The skewness of the BBS was -1.57, and the full score was 35%, indicating a ceiling effect. However, no ceiling or floor effect was observed for the Mini-BESTest and the Brief-BESTest. Intraclass correlation coefficients for intra-rater and inter-rater reliabilities were 0.98 and 0.97 for the Mini-BESTest and Brief-BESTest, respectively. Individual item reliability was moderate or better for the Mini-BESTest and excellent or better for the Brief-BESTest. The MDC of total scores ranged 3.14-3.84 and 2.92-3.60 for the Mini-BESTest and Brief-BESTest, respectively.

CONCLUSIONS

The Mini-BESTest and Brief-BESTest are reliable assessment tools for patients with acute and subacute incomplete SCI classified as AIS D. Clarified error ranges aid in estimating the treatment effect on balance abilities.

In-Clinic and Natural Gait Observations master protocol (I-CAN-GO) to validate gait using a lumbar accelerometer

Traditional measurements of gait are typically performed in clinical or laboratory settings where functional assessments are used to collect episodic data, which may not reflect naturalistic gait and activity patterns. The emergence of digital health technologies has enabled reliable and continuous representation of gait and activity in free-living environments. To provide further evidence for naturalistic gait characterization, we designed a master protocol to validate and evaluate the performance of a method for measuring gait derived from a single lumbar-worn accelerometer with respect to reference methods. This evaluation included distinguishing between participants' self-perceived different gait speed levels, and effects of different floor surfaces such as carpet and tile on walking performance, and performance under different bouts, speed, and duration of walking during a wide range of simulated daily activities. Using data from 20 healthy adult participants, we found different self-paced walking speeds and floor surface effects can be accurately characterized. Furthermore, we showed accurate representation of gait and activity during simulated daily living activities and longer bouts of outside walking. Participants in general found that the devices were comfortable. These results extend our previous validation of the method to more naturalistic setting and increases confidence of implementation at-home.

SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies

Background/objective

Recent technological advances have allowed for the development of smart wearable devices (SmartWear) which can be used to monitor various aspects of patient healthcare. These devices provide clinicians with continuous biometric data collection for patients in both inpatient and outpatient settings. Although these devices have been widely used in fields such as cardiology and orthopedics, their use in the field of neurosurgery and neurology remains in its infancy.

Methods

A comprehensive literature search for the current and future applications of SmartWear devices in the above conditions was conducted, focusing on outpatient monitoring.

Findings

Through the integration of sensors which measure parameters such as physical activity, hemodynamic variables, and electrical conductivity - these devices have been applied to patient populations such as those at risk for stroke, suffering from epilepsy, with neurodegenerative disease, with spinal cord injury and/or recovering from neurosurgical procedures. Further, these devices are being tested in various clinical trials and there is a demonstrated interest in the development of new technologies.

Conclusion

This review provides an in-depth evaluation of the use of SmartWear in selected neurological diseases and neurosurgical applications. It is clear that these devices have demonstrated efficacy in a variety of neurological and neurosurgical applications, however challenges such as data privacy and management must be addressed.

Spatial characteristics of reactive stepping among people living with chronic incomplete spinal cord injury

Objective: Compare the spatial characteristics of reactive stepping between individuals with chronic motor incomplete spinal cord injuries (iSCI) and able-bodied (AB) individuals.Design: Cross sectional.Setting: Lyndhurst Centre.Participants: Twelve individuals with iSCI (3 males, 53.6 ± 15.2 years old) and 11 age- and sex-matched AB individuals (3 males, 54.8 ± 14.0 years old).Interventions: The Lean-and-Release test was used to elicit reactive stepping. A horizontal cable, attached at waist height, was released when 8-12% body weight was supported in a forward lean position. Participants underwent up to 10 Lean-and-Release trials in a session. Kinematic and kinetic data were recorded.Outcome measures: The length, width and height of the first reactive step of each trial were calculated. Standard deviation between trials was calculated to represent the variability in step length, width and height within a participant. Among participants with iSCI, correlation coefficients were used to explore the relationship between step length and width variability and (1) Lean-and-Release test behavioral responses, (2) 3-month fall history, and (3) lower extremity strength.Results: Step length (P = 0.94), width (P = 0.52) and height (P = 0.97), normalized for participant height, did not differ between groups. Participants with iSCI showed greater variability in step length (P = 0.02) and width (P = 0.01), but not height (P = 0.32). No correlation was found between step length or width variability and behavioral responses, 3-month fall history, or lower extremity strength.Conclusions: Individuals with iSCI showed increased variability in length and width of reactive stepping compared to AB individuals, which may contribute to their impaired ability to execute single-step reactive responses.Trial registration: ClinicalTrials.gov identifier: NCT02960178.

Failure Analysis for Gold Wire Bonding of Sensor Packaging Based on Experimental and Numerical Methods

There is an increasing demand for the use of automotive sensors where complex working environments may easily lead to failure. Wire pull and shear test models based on finite-element analysis are established to evaluate their reliability by investigating the failure mode and mechanism of gold wire bonding. The effect of shear force position and pull force position on failure is also analyzed. The bonding failure was verified by experiments, which is consistent with the simulation result. The results show that: (1) The three-dimensional quantitative modeling reveals the process of bonding delamination and stress concentration. (2) The bonding-slip method (BSM) is adopted in the gold ball detaching process. The concept of three states, including deformation accumulation, cracking, and disengagement, was put forward to reveal the interface stress evolution trend according to the shear testing results. The results indicate that in the interface, the stress in the deformation accumulation state decreases from the tensile side (or compression side) to the center, and the stress in the cracking and disengagement states reduces gradually from the tensile side to the edge. When the interface is completely separated, the failed shear force concentrates on 42 g. The concept and theory proposed in this work can effectively reveal the failure mechanism of bonding interface and help to establish a new failure criterion.

Data-driven characterization of walking after a spinal cord injury using inertial sensors

BACKGROUND

An incomplete spinal cord injury (SCI) refers to remaining sensorimotor function below the injury with the possibility for the patient to regain walking abilities. However, these patients often suffer from diverse gait deficits, which are not objectively assessed in the current clinical routine. Wearable inertial sensors are a promising tool to capture gait patterns objectively and started to gain ground for other neurological disorders such as stroke, multiple sclerosis, and Parkinson's disease. In this work, we present a data-driven approach to assess walking for SCI patients based on sensor-derived outcome measures. We aimed to (i) characterize their walking pattern in more depth by identifying groups with similar walking characteristics and (ii) use sensor-derived gait parameters as predictors for future walking capacity.

METHODS

The dataset analyzed consisted of 66 SCI patients and 20 healthy controls performing a standardized gait test, namely the 6-min walking test (6MWT), while wearing a sparse sensor setup of one sensor attached to each ankle. A data-driven approach has been followed using statistical methods and machine learning models to identify relevant and non-redundant gait parameters.

RESULTS

Clustering resulted in 4 groups of patients that were compared to each other and to the healthy controls. The clusters did differ in terms of their average walking speed but also in terms of more qualitative gait parameters such as variability or parameters indicating compensatory movements. Further, using longitudinal data from a subset of patients that performed the 6MWT several times during their rehabilitation, a prediction model has been trained to estimate whether the patient's walking speed will improve significantly in the future. Including sensor-derived gait parameters as inputs for the prediction model resulted in an accuracy of 80%, which is a considerable improvement of 10% compared to using only the days since injury, the present 6MWT distance, and the days until the next 6MWT as predictors.

CONCLUSIONS

In summary, the work presented proves that sensor-derived gait parameters provide additional information on walking characteristics and thus are beneficial to complement clinical walking assessments of SCI patients. This work is a step towards a more deficit-oriented therapy and paves the way for better rehabilitation outcome predictions.

Utilizing Data from Wearable Technologies in the Era of Telemedicine to Assess Patient Function and Outcomes in Neurosurgery: Systematic Review and Time-Trend Analysis of the Literature

BACKGROUND

The COVID-19 pandemic has driven the increased use of telemedicine and the adoption of wearable technology in neurosurgery. We reviewed studies exploring the use of wearables on neurosurgical patients and analyzed wearables' scientific production trends.

METHODS

The review encompassed PubMed, EMBASE, Web of Science, and Cochrane Library. Bibliometric analysis was performed using citation data of the included studies through Elsevier's Scopus database. Linear regression was utilized to understand scientific production trends. All analyses were performed on R 4.1.2.

RESULTS

We identified 979 studies. After screening, 49 studies were included. Most studies evaluated wearable technology use for patients with spinal pathology (n = 31). The studies were published over a 24-year period (1998-2021). Forty-seven studies involved wearable device use relevant to telemedicine. Bibliometric analysis revealed a compounded annual growth rate of 7.3%, adjusted for inflation, in annual scientific production from 1998 to 2021 (coefficient=1.3; 95% Confidence Interval = [0.7, 1.9], P < 0.01). Scientific production steadily increased in 2014 (n = 1) and peaked from 2019 (n = 8) to 2021 (n = 13) in correlation with the COVID-19 pandemic. Publications spanned 34 journals, averaged 24.4 citations per article, 3.0 citations per year per article, and 8.3 authors per article.

CONCLUSION

Wearables can provide clinicians with objective measurements to determine patient function and quality of life. The rise in articles related to wearables in neurosurgery demonstrates the increased adoption of wearable devices during the COVID-19 pandemic. Wearable devices appear to be a key component in this era of telemedicine and their positive utility and practicality are increasingly being realized in neurosurgery.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

Sensing of joint and spinal bending or stretching via a retractable and wearable badge reel

Human motions, such as joint/spinal bending or stretching, often contain information that is useful for orthopedic/neural disease diagnosis, rehabilitation, and prevention. Here, we show a badge-reel-like stretch sensing device with a grating-structured triboelectric nanogenerator exhibiting a stretching sensitivity of 8 V mm⁻¹, a minimum resolution of 0.6 mm, a low hysteresis, and a high durability (over 120 thousand cycles). Experimental and theoretical investigations are performed to define the key features of the device. Studies from human natural daily activities and exercise demonstrate the functionality of the sensor for real-time recording of knee/arm bending, neck/waist twisting, and so on. We also used the device in a spinal laboratory, monitoring human subjects’ spine motions, and validated the measurements using the commercial inclinometer and hunchback instrument. We anticipate that the lightweight, precise and durable stretch sensor applied to spinal monitoring could help mitigate the risk of long-term abnormal postural habits induced diseases.

Human motions often contain information that is useful for orthopedic/neural disease diagnosis, rehabilitation, and prevention. Here, the authors show a badge-reel-like stretch sensing device with a grating-structured triboelectric nanogenerator for joints/spine bending or stretching sensing.