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Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform fall risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living fall risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data in order to better inform free-living fall risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform fall risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own fall risk. This second aspect of the study is important as, traditionally, there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness and negating any public stigma on the use of research-style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy, which could overcome concerns with the adoption of video to better inform IMU-based gait and free-living fall risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to fall risk that PwPD recognise as helpful and safe to use. [ABSTRACT FROM AUTHOR]

This document is a compilation of research articles on gait analysis and running biomechanics. It covers topics such as the use of wearables for gait analysis, the effects of various factors on running patterns, and the reliability and validity of different measurement methods. The articles include studies on different populations, including neurological patients, elderly individuals, and runners. This comprehensive resource can be valuable for library patrons conducting research on gait analysis and running biomechanics. [Extracted from the article]

Quantitative running gait analysis is an important tool that provides beneficial outcomes to injury risk/recovery or performance assessment. Wearable devices have allowed running gait to be evaluated in any environment (i.e., laboratory or real-world settings), yet there are a plethora of different grades of devices (i.e., research-grade, commercial, or novel multi-modal) available with little information to make informed decisions on selection. This paper outlines a protocol that will examine different grades of wearables for running gait analysis in healthy individuals. Specifically, this pilot study will: 1) examine analytical validity and reliability of wearables (research-grade, commercial, high-end multimodal) within a controlled laboratory setting; 2) examine analytical validation of different grades of wearables in a real-world setting, and 3) explore clinical validation and usability of wearables for running gait analysis (e.g., injury history (previously injured, never injured), performance level (novice, elite) and relationship to meaningful outcomes). The different grades of wearable include: (1) A research-grade device, the Ax6 consists of a configurable tri-axial accelerometer and tri-axial gyroscope with variable sampling capabilities; (2) attainable (low-grade) commercial with proprietary software, the DorsaVi ViMove2 consisting of two, non-configurable IMUs modules, with a fixed sampling rate and (3) novel multimodal high-end system, the DANU Sports System that is a pair of textile socks, that contain silicone based capacitive pressure sensors, and configurable IMU modules with variable sampling rates. Clinical trial registration: Trial registration: NCT05277181. [ABSTRACT FROM AUTHOR]

Copper (I) sulfide (Cu2S) is a low-cost, earth-abundant, and non-toxic thermoelectric material for applications in the middle–high temperature range (>650 K). Although 3D printing these materials can simplify their manufacturing, elevated temperatures observed during sintering impair their crystal structure and energy conversion efficiency. In this study, we demonstrated a novel post-processing methodology to revert the thermoelectric properties of the 3D printed Cu2-xS materials back to the unimpaired state via sulfur infusion. After printing and sintering, sulfur was infused into the specimens under vacuum to optimize their crystal structure and achieve high thermoelectric efficiency. Chemical analysis and X-ray Diffraction (XRD) tests showed that after the sulfur infusion process, the Cu/S ratio was reverted close to the stoichiometric level. The 3D printed Cu2-xS showed p-type thermoelectric behavior with electrical conductivity peaking at 143 S-cm−1 at 750 K and Seebeck coefficient of 175 µV-K−1 at 627 K. The figure of merit (ZT) value of 1.0 at 780 K was achieved, which is the highest value ever reported for a 3D printed Cu2-xS thermoelectrics at this temperature. The fabrication of environmentally friendly thermoelectric materials with extended dimensional freedom and conversion efficiency has the potential to impact the thermoelectric industry with new energy conversion applications and lowered manufacturing costs. [ABSTRACT FROM AUTHOR]

Gait speed declines with age and slower walking speeds are associated with poor health outcomes. Understanding why we do not walk faster as we age, despite being able to, has implications for rehabilitation. Changes in regional oxygenated haemoglobin (HbO2) across the frontal lobe were monitored using functional near infrared spectroscopy in 17 young and 18 older adults while they walked on a treadmill for 5 min, alternating between 30 s of walking at a preferred and fast (120% preferred) speed. Gait was quantified using a triaxial accelerometer (lower back). Differences between task (preferred/fast) and group (young/old) and associations between regional HbO2 and gait were evaluated. Paired tests indicated increased HbO2 in the supplementary motor area (right) and primary motor cortex (left and right) in older adults when walking fast (p < 0.006). HbO2 did not significantly change in the young when walking fast, despite both groups modulating gait. When evaluating the effect of age (linear mixed effects model), greater increases in HbO2 were observed for older adults when walking fast (prefrontal cortex, premotor cortex, supplementary motor area and primary motor cortex) compared to young adults. In older adults, increased step length and reduced step length variability were associated with larger increases in HbO2 across multiple regions when walking fast. Walking fast required increased activation of motor regions in older adults, which may serve as a therapeutic target for rehabilitation. Widespread increases in HbO2 across the frontal cortex highlight that walking fast represents a resource-intensive task as we age. [ABSTRACT FROM AUTHOR]

Background: Mobile applications and technology (e.g., stroboscopic glasses) are increasingly being used to deliver combined visual and cognitive (termed visuo-cognitive) training that replaces standard pen and paper-based interventions. These 'technological visuo-cognitive training' (TVT) interventions could help address the complex problems associated with visuo-cognitive dysfunction in people with long term neurological conditions such as Parkinson's disease. As data emerges to support the effectiveness of these technologies, patient perspectives offer an insight into how novel TVT is received by people living with long term neurological conditions. Objective: To explore experiences of people with Parkinson's in using technology as part of a home-based visuo-cognitive training programme compared to traditional approaches to rehabilitation. Methods: Eight people with Parkinson's who took part in a pilot randomised cross-over trial, investigating the efficacy and feasibility of TVT compared to standard care, were interviewed to explore their experiences of each arm of the training they received. Integration of Normalisation Process Theory (NPT) into the analysis enabled examination of the potential to embed novel TVT into a home-based rehabilitation intervention for people with Parkinson's disease. Results: Three key themes emerged from the thematic analysis as factors influencing the implementation potential of TVT for people with Parkinson's disease: perceived value of technology, perceived ease of use and support mechanisms. Further examination of the data through the lens of NPT revealed that the implantation and embedding of novel technology was dependent on positive user experience, individual disease manifestation and engagement with a professional. Conclusions: Our findings provide insights into the challenges of engaging with technology-based interventions while living with a progressive and fluctuating disease. When implementing technology-based interventions for people with Parkinson's, we recommend that patients and clinicians collaborate to determine whether the technology fits the capacity, preference, and treatment needs of the individual patient. [ABSTRACT FROM AUTHOR]

Although the multifactorial nature of falls in Parkinson's disease (PD) is well described, optimal assessment for the identification of fallers remains unclear. Thus, we aimed to identify clinical and objective gait measures that best discriminate fallers from non-fallers in PD, with suggestions of optimal cutoff scores. METHODS: Individuals with mild-to-moderate PD were classified as fallers (n = 31) or non-fallers (n = 96) based on the previous 12 months' falls. Clinical measures (demographic, motor, cognitive and patient-reported outcomes) were assessed with standard scales/tests, and gait parameters were derived from wearable inertial sensors (Mobility Lab v2); participants walked overground, at a self-selected speed, for 2 min under single and dual-task walking conditions (maximum forward digit span). Receiver operating characteristic curve analysis identified measures (separately and in combination) that best discriminate fallers from non-fallers; we calculated the area under the curve (AUC) and identified optimal cutoff scores (i.e., point closest-to-(0,1) corner). RESULTS: Single gait and clinical measures that best classified fallers were foot strike angle (AUC = 0.728; cutoff = 14.07°) and the Falls Efficacy Scale International (FES-I; AUC = 0.716, cutoff = 25.5), respectively. Combinations of clinical + gait measures had higher AUCs than combinations of clinical-only or gait-only measures. The best performing combination included the FES-I score, New Freezing of Gait Questionnaire score, foot strike angle and trunk transverse range of motion (AUC = 0.85). CONCLUSION: Multiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD. [ABSTRACT FROM AUTHOR]

Aging is a key risk factor for the development of Parkinson’s disease (PD). PD is characterized by excessive synchrony of beta oscillations (13–30 Hz) in the basal ganglia thalamo-cortical network. However, cortical beta power is not reliably elevated in individuals with PD. Here, we sought to disentangle how resting cortical beta power compares in younger controls, older controls, and individuals with PD using scalp electroencephalogram (EEG) and a novel approach for quantifying beta power. Specifically, we used a Gaussian model to determine if sensorimotor beta power distinguishes these groups. In addition, we looked at the distribution of beta power across the entire cortex. Our findings showed that Gaussian-modeled beta power does not differentiate individuals with PD (on medication) from healthy younger or older controls in sensorimotor cortex. However, beta power (and not theta or alpha) was higher in healthy older versus younger controls. This effect was most pronounced in regions near sensorimotor cortex including the frontal and parietal areas [P < 0.05, false discovery rate (FDR) corrected]. In addition, the bandwidth of the periodic beta was also higher in healthy older than young individuals in parietal regions. Finally, the aperiodic component, specifically the exponent of the signal, was higher (steeper) in younger controls than in individuals with PD in the right parietal-occipital region (P < 0.05, FDR corrected), possibly reflecting differences in neuronal spiking. Our findings suggest that cortical Gaussian beta power is possibly modulated by age and could be further explored in longitudinal studies to determine whether sensorimotor beta increases with increasing age. NEW & NOTEWORTHY Altered sensorimotor beta activity has been shown to be a feature in aging and PD. Using a novel approach, we clarify that resting sensorimotor beta power does not distinguish subjects with PD from healthy younger and older controls. However, beta power was higher in older compared with younger controls in central sensorimotor, frontal, and parietal regions. These results provide a clearer picture of sensorimotor beta power, demonstrating that it is elevated in aging but not PD. [ABSTRACT FROM AUTHOR]

Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment. [ABSTRACT FROM AUTHOR]

Strobe/quad strobe glasses People with PD (and older adults) become increasingly reliant on visuo-cognition for mobility, which may require sensory reweighting training to overcome functional problems and improve performance. The "Senaptec App" is a portable training extension of the SSS assessment/rehabilitation, which can be used on a tablet within any environment and works on both Android and iOS platforms (Figure 1c). Parkinson's disease (PD) commonly affects both visual and cognitive functions, which is complicated by the fact that these functions are inter-related (termed visuo-cognition). If the findings demonstrate that the Senaptec solutions are useful for visuo-cognitive assessments and rehabilitation in PD, this would support their use as a potential means of objectively and comprehensively providing visuo-cognitive care for patients in a variety of settings. [Extracted from the article]

Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches with IMUs alone remain limited, as there are no contextual data to comprehensively determine if underlying mechanistic (intrinsic) or environmental (extrinsic) factors impact mobility and, therefore, fall risk. Here, a case study is used to explore and discuss how contemporary video-based wearables could be used to supplement arising mobility-based IMU gait data to better inform habitual fall risk assessment. A single stroke survivor was recruited, and he conducted a series of mobility tasks in a lab and beyond while wearing video-based glasses and a single IMU. The latter generated topical gait characteristics that were discussed according to current research practices. Although current IMU-based approaches are beginning to provide habitual data, they remain limited. Given the plethora of extrinsic factors that may influence mobility-based gait, there is a need to corroborate IMUs with video data to comprehensively inform fall risk assessment. Use of artificial intelligence (AI)-based computer vision approaches could drastically aid the processing of video data in a timely and ethical manner. Many off-the-shelf AI tools exist to aid this current need and provide a means to automate contextual analysis to better inform mobility from IMU gait data for an individualized and contemporary approach to habitual fall risk assessment. [ABSTRACT FROM AUTHOR]

Running gait assessment is essential for the development of technical optimization strategies as well as to inform injury prevention and rehabilitation. Currently, running gait assessment relies on (i) visual assessment, exhibiting subjectivity and limited reliability, or (ii) use of instrumented approaches, which often carry high costs and can be intrusive due to the attachment of equipment to the body. Here, the use of an IoT-enabled markerless computer vision smartphone application based upon Google's pose estimation model BlazePose was evaluated for running gait assessment for use in low-resource settings. That human pose estimation architecture was used to extract contact time, swing time, step time, knee flexion angle, and foot strike location from a large cohort of runners. The gold-standard Vicon 3D motion capture system was used as a reference. The proposed approach performs robustly, demonstrating good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all running gait outcomes. Additionally, temporal outcomes exhibit low mean error (0.01–0.014 s) in left foot outcomes. However, there are some discrepancies in right foot outcomes, due to occlusion. This study demonstrates that the proposed low-cost and markerless system provides accurate running gait assessment outcomes. The approach may help routine running gait assessment in low-resource environments. [ABSTRACT FROM AUTHOR]

Background: Turning is a complex measure of gait that accounts for over 50% of daily steps. Traditionally, turning has been measured in a research grade laboratory setting, however, there is demand for a low-cost and portable solution to measure turning using wearable technology. This study aimed to determine the suitability of a low-cost inertial sensor-based device (AX6, Axivity) to assess turning, by simultaneously capturing and comparing to a turn algorithm output from a previously validated reference inertial sensor-based device (Opal), in healthy young adults. Methodology: Thirty participants (aged 23.9 ± 4.89 years) completed the following turning protocol wearing the AX6 and reference device: a turn course, a two-minute walk (including 180° turns) and turning in place, alternating 360° turn right and left. Both devices were attached at the lumbar spine, one Opal via a belt, and the AX6 via double sided tape attached directly to the skin. Turning measures included number of turns, average turn duration, angle, velocity, and jerk. Results: Agreement between the outcomes from the AX6 and reference device was good to excellent for all turn characteristics (all ICCs > 0.850) during the turning 360° task. There was good agreement for all turn characteristics (all ICCs > 0.800) during the two-minute walk task, except for moderate agreement for turn angle (ICC 0.683). Agreement for turn outcomes was moderate to good during the turns course (ICCs range; 0.580 to 0.870). Conclusions: A low-cost wearable sensor, AX6, can be a suitable and fit-for-purpose device when used with validated algorithms for assessment of turning outcomes, particularly during continuous turning tasks. Future work needs to determine the suitability and validity of turning in aging and clinical cohorts within low-resource settings. [ABSTRACT FROM AUTHOR]

Various cueing strategies (internal and external) have been used to alleviate gait deficits in Parkinson's disease (PD). However, it remains unclear which type of cueing strategy is most effective at different disease stages or with more severe walking impairment, such as freezing of gait (FOG). The underlying neural mechanisms of response to cueing are also unknown. This trial aims to: (i) determine brain activity response to cue stimulus (internal, visual, auditory or tactile) when walking in PD and; (ii) examine changes in brain activity to cues at different stages of PD. This ongoing single-site study uses an exploratory observational design, with laboratory application of cues for gait deficit. A total of 80 people with PD who meet the inclusion criteria will be enrolled. Participants are split into groups dependent on their disease stage (classified with the Hoehn and Yahr (H&Y) scale); n = 20 H&YI; n = 30 H&YII; n = 30 H&YIII. Within the H&Y stage II and III groups, we will also ensure recruitment of a sub-group of 15 individuals with FOG within each group. Participants perform walking tasks under several conditions: baseline walking without cues; randomized cued walking conditions [internal and external (visual, auditory and tactile) cues]. A combined functional near-infrared spectroscopy and electroencephalography system quantifies cortical brain activity while walking. Inertial sensors are used to assess gait. Primary outcome measures are cue-related changes in cortical brain activity while walking, including the relative change in cortical HbO2 and the power spectral densities at alpha (8-13Hz), beta (13-30Hz), delta (0.5-4Hz), theta (4-8Hz) and gamma (30-40Hz) frequency bandwidths. Secondary outcome measures are cue-related changes in spatiotemporal gait characteristics. Findings will enhance our understanding about the cortical responses to different cueing strategies and how they are influenced by PD progression and FOG status. This trial is registered at clinicaltrials.gov (NCT04863560; April 28, 2021, https://clinicaltrials.gov/ct2/show/NCT04863560). [ABSTRACT FROM AUTHOR]

Visual and cognitive dysfunction are common in Parkinson's disease and relate to balance and gait impairment, as well as increased falls risk and reduced quality of life. Vision and cognition are interrelated (termed visuo-cognition) which makes intervention complex in people with Parkinson's (PwP). Non-pharmacological interventions for visuo-cognitive deficits are possible with modern technology, such as combined mobile applications and stroboscopic glasses, but evidence for their effectiveness in PwP is lacking. We aim to investigate whether technological visuo-cognitive training (TVT) can improve visuo-cognitive function in PwP. We will use a parallel group randomised controlled trial to evaluate the feasibility and acceptability of TVT versus standard care in PwP. Forty PwP who meet our inclusion criteria will be randomly assigned to one of two visuo-cognitive training interventions. Both interventions will be carried out by a qualified physiotherapist in participants own homes (1-hour sessions, twice a week, for 4 weeks). Outcome measures will be assessed on anti-parkinsonian medication at baseline and at the end of the 4-week intervention. Feasibility of the TVT intervention will be assessed in relation to safety and acceptability of the technological intervention, compliance and adherence to the intervention and usability of equipment in participants homes. Additionally, semi structured interviews will be conducted to explore participants' experience of the technology. Exploratory efficacy outcomes will include change in visual attention measured using the Trail Making Test as well as changes in balance, gait, quality of life, fear of falling and levels of activity. This pilot study will focus on the feasibility and acceptability of TVT in PwP and provide preliminary data to support the design of a larger, multi-centre randomised controlled trial. This trial is registered at isrctn.com (ISRCTN46164906). [ABSTRACT FROM AUTHOR]

Turning is a common impairment of mobility in people with Parkinson's disease (PD), which increases freezing of gait (FoG) episodes and has implications for falls risk. Visual cues have been shown to improve general gait characteristics in PD. However, the effects of visual cues on turning deficits in PD remains unclear. We aimed to (i) compare the response of turning performance while walking (180° and 360° turns) to visual cues in people with PD with and without FoG; and (ii) examine the relationship between FoG severity and response to visual cues during turning. This exploratory interventional study measured turning while walking in 43 participants with PD (22 with self-reported FoG) and 20 controls using an inertial sensor placed at the fifth lumbar vertebrae region. Participants walked straight and performed 180° and 360° turns midway through a 10 m walk, which was done with and without visual cues (starred pattern). The turn duration and velocity response to visual cues were assessed using linear mixed effects models. People with FoG turned slower and longer than people with PD without FoG and controls (group effect: p < 0.001). Visual cues reduced the velocity of turning 180° across all groups and reduced the velocity of turning 360° in people with PD without FoG and controls. FoG severity was not significantly associated with response to visual cues during turning. Findings suggest that visual cueing can modify turning during walking in PD, with response influenced by FoG status and turn amplitude. Slower turning in response to visual cueing may indicate a more cautious and/or attention-driven turning pattern. This study contributes to our understanding of the influence that cues can have on turning performance in PD, particularly in freezers, and will aid in their therapeutic application. [ABSTRACT FROM AUTHOR]

• Neck posture improved immediately with the UpRight in Parkinson's disease (PD) during sitting and standing. • Neck, trunk and low back posture did not improve with the UpRight during walking in PD. • Gait was worse when walking with the UpRight device compared to without in PD. • Postural improvement related to attentional capabilities of people with PD. • Self-reported feedback suggests that participants felt the UpRight was beneficial. This interventional pilot study aimed to 1) examine whether a novel wearable vibro-tactile feedback device ('UpRight Go') is effective and feasible to improve postural alignment in Parkinson's disease (PD); 2) explore relationships between postural alignment and attention in PD; 3) explore effect of vibro-tactile device on balance and gait; and 4) gain initial feedback on the use of the vibro-tactile device in the laboratory and at home. 25 people with PD sat, stood and walked for two-minutes without and with the UpRight device attached to their upper backs to provide feedback on postural alignment in the laboratory. A sub-group (n = 12) wore the UpRight device at home for 60 min. per day for 7-days of postural feedback. Subjective feedback on use of the device was obtained in the laboratory and at the end of the 7-day period. The primary outcome for this study was posture measured by verticality of inertial measurement units (IMUs) at the neck, trunk and low back, which was done with and without the UpRight device. Secondary outcomes included clinical measures of posture, subjective feedback on the device, computerized attention measures, gait and balance. Neck postural alignment in PD was significantly improved (reduced neck flexion) with the UpRight during sitting and standing in both clinical measures (p = 0.005) and IMU outcomes (p = 0.046), but trunk and low back posture did not change. There was no change in postural alignment during walking with the UpRight. Postural alignment response was related to attentional capabilities. Many subjects (68 %) reported that they felt a benefit from the UpRight and most participants reported that the device was acceptable (Lab use; 72 %, Home use; 75 %). The UpRight Go feedback device may improve neck/upper-back posture in PD during sitting and standing, but not during walking. Postural alignment response to the device may depend on attentional mechanisms. [ABSTRACT FROM AUTHOR]

Objectives: This interventional pilot study aimed to 1) examine whether a novel wearable vibro-tactile feedback device ('UpRight Go') is effective and feasible to improve postural alignment in Parkinson's disease (PD); 2) explore relationships between postural alignment and attention in PD; 3) explore effect of vibro-tactile device on balance and gait; and 4) gain initial feedback on the use of the vibro-tactile device in the laboratory and at home.Methods: 25 people with PD sat, stood and walked for two-minutes without and with the UpRight device attached to their upper backs to provide feedback on postural alignment in the laboratory. A sub-group (n = 12) wore the UpRight device at home for 60 min. per day for 7-days of postural feedback. Subjective feedback on use of the device was obtained in the laboratory and at the end of the 7-day period. The primary outcome for this study was posture measured by verticality of inertial measurement units (IMUs) at the neck, trunk and low back, which was done with and without the UpRight device. Secondary outcomes included clinical measures of posture, subjective feedback on the device, computerized attention measures, gait and balance.Results: Neck postural alignment in PD was significantly improved (reduced neck flexion) with the UpRight during sitting and standing in both clinical measures (p = 0.005) and IMU outcomes (p = 0.046), but trunk and low back posture did not change. There was no change in postural alignment during walking with the UpRight. Postural alignment response was related to attentional capabilities. Many subjects (68 %) reported that they felt a benefit from the UpRight and most participants reported that the device was acceptable (Lab use; 72 %, Home use; 75 %).Conclusion: The UpRight Go feedback device may improve neck/upper-back posture in PD during sitting and standing, but not during walking. Postural alignment response to the device may depend on attentional mechanisms. [ABSTRACT FROM AUTHOR]