Posts Tagged Motion analysis

[WEB SITE] MindMaze buys Gait Up to add motion analysis to its VR platform

Some consolidation is afoot in the world of virtual reality. MindMaze, a startup based out of Switzerland that has been building a VR platform to map and respond to brain activity for applications in healthcare and other fields, is making its first acquisition: Gait Up, a developer of motion analytics used in smart watches and other hardware.

Terms of the deal are not being disclosed, MindMaze’s CEO and founder Tej Tadi said in an interview. Gait Up was bootstrapped and generating revenue and had an interesting list of customers and partners, including Logitech, the Swatch group, Hitachi, Philips, Salomon, PIQ, BNP Paribas group, Jaguar Land Rover and Pomoca.

The company’s 10 or so employees will be joining MindMaze as part of the deal, and Gait Up will continue to serve its existing customers for the rest of this year before gradually working to integrate its tech with that of MindMaze’s to expand its platform to bring in more diagnostics and data.

“From the research we began 15 years ago, our vision has been to change the way we measure and make sense of human motion,” said Benoit Mariani, CEO and co-founder of Gait Up in a statement. “Adding our motion tracking technology to MindMaze’s suite of solutions creates exciting new possibilities for industries far beyond our healthcare roots.”

The acquisition comes at a time when MindMaze has seen some interesting developments of its own. Earlier this year, the company received FDA approval in the U.S. so that it can start to sell its technology to healthcare organizations in that country.

It has also completed work on new hardware, a piece of foam that lines a VR headset with sensors to pick up your facial expressions and translate them directly to your avatar on the screen.

And, we have discovered that MindMaze is in the process of raising a new round of funding for its next phase of development. That round will be at least as big as its previous round — MindMaze notably raised $100 million in February 2016 — and possibly more, I understand.

Tadi tells me that he had his eye on Gait Up for a while before making the acquisition: both companies are based out of Lausanne and he had been watching it build its technology gradually and acquire customers before making a move.

Gait Up’s founding followed a route that you see many of the most interesting startups in areas like AI and machine learning take in Switzerland: as an offshoot of academic work. In its case, it was started as a joint venture between the University Hospital of Lausanne and the Swiss Institute of Technology of Lausanne, and has the academic cred to prove it with some 358 articles published in academic journals and with several patents locked down on top of that.

Similar to MindMaze, Gait Up had an ambition to build both a software platform coupled with hardware developments, in its case, in the area of analyzing human movement in very precise increments using inertial sensors, or systems that combine a mix of computers, motion sensors like accelerometers and rotation sensors to calculate movement without the need for external references.

Inertial sensors are often used in ships, cars and other vehicles, and while that points to areas where MindMaze might hope to apply the tech itself, it’s also notable that Gait Up’s marquee product, the Droplet motion sensor, is the smallest inertial sensor ever made, making it a good fit for human movement and plans that MindMaze may have for its own product roadmap in headsets and other wearables and hand-held devices.

“For VR/AR to achieve mainstream adoption, having an immersive user experience powered by mobile devices is essential; we’ve solved one of the most complex aspects of virtual immersion, human emotion, when we launched MASK in early 2017,” observed Tadi. “Now with Gait Up’s unrivaled motion analysis technology, we’ll transport human movement in all its dynamic range to virtual worlds.”

via MindMaze buys Gait Up to add motion analysis to its VR platform | TechCrunch

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[ARTICLE] Effects of Adjuvant Mental Practice on Affected Upper Limb Function Following a Stroke: Results of Three-Dimensional Motion Analysis, Fugl-Meyer Assessment of the Upper Extremity and Motor Activity Logs – Full Text HTML



Objective To investigate the effects of adjuvant mental practice (MP) on affected upper limb function following a stroke using three-dimensional (3D) motion analysis.

Methods In this AB/BA crossover study, we studied 10 hemiplegic patients who had a stroke within the past 6 months. The patients were randomly allocated to two groups: one group received MP combined with conventional rehabilitation therapy for the first 3 weeks followed by conventional rehabilitation therapy alone for the final 3 weeks; the other group received the same therapy but in reverse order. The MP tasks included drinking from a cup and opening a door. MP was individually administered for 20 minutes, 3 days a week for 3 weeks. To assess the tasks, we used 3D motion analysis and three additional tests: the Fugl-Meyer Assessment of the upper extremity (FMA-UE) and the motor activity logs for amount of use (MAL-AOU) and quality of movement (MAL-QOM). Assessments were performed immediately before treatment (T0), 3 weeks into treatment (T1), and 6 weeks into treatment (T2).

Results Based on the results of the 3D motion analysis and the FMA-UE index (p=0.106), the MAL-AOU scale (p=0.092), and MAL-QOM scale (p=0.273), adjuvant MP did not result in significant improvements.

Conclusion Adjuvant MP had no significant effect on upper limb function following a stroke, according to 3D motion analysis and three clinical assessment tools (the FMA-UE index and the two MAL scales). The importance of this study is its use of objective 3D motion analysis to evaluate the effects of MP. Further studies will be needed to validate these findings.


Stroke patients with dysfunction of the upper extremities can face significant problems in their activities of daily living (ADLs) as well as in the recovery of other general functions [1].

Although many different therapeutic approaches are available for improving upper extremity function after a stroke [1], it is important to select the most appropriate intervention for rehabilitation in accordance with the severity of impairment.

Mental imagery is an active process that combines all six senses: visual, auditory, tactile, kinesthetic, olfactory, and gustatory [2]. Motor imagery, a component of mental imagery, is associated with a specific movement produced by the internal reproduction of motor action without motor output [2, 3]. Mental practice (MP) involves motor imagery and includes repetitive imagination of a physical activity with the intention of performing that activity or improving performance [2, 4]. MP allows an individual to perform tasks repeatedly without physical exhaustion or any risk to safety [5]. In addition, it enables patients to practice complex physical tasks that the stroke had rendered difficult.

MP was first used in sports to improve techniques, and it is believed that neural loops and movement patterns may be activated during MP [1]. The application of MP in stroke patients was reported to activate the cerebral and cerebellar sensorimotor structures repeatedly [6], and similar results were obtained when the actual tasks were practiced, according to a study involving positron emission tomography [7]. Another study [8] showed that MP increased activity in the premotor area, the primary motor cortex, and the superior parietal cortex. In patients receiving hemiplegic stroke rehabilitation, the application of MP along with other neurological practices was shown to help recovery of unilateral upper limb function at a low cost and without risks or complications [8, 9, 10].

Based on a review of the Cochrane database in 2011 (6 trials, n=119), the use of rehabilitation treatments combined with MP was found to be more effective for improving upper extremity function after stroke than were rehabilitation treatments without MP [4]. Previous studies assessed MP for accomplishing ADLs (such as ironing or buttoning a shirt, turning a page in a book, lifting a cup, or opening a door). However, results of several studies using the Fugl-Meyer Assessment of the upper extremity (FMA-UE), the action research arm test (ARAT), and the motor activity log (MAL) to evaluate muscle power and hand function indicated a mismatch between the intervention and evaluation methods [10, 11, 12].

Conventional studies [10, 11, 13, 14] have shown that upper extremity function can be improved with adjunctive MP; however, in these studies, the tasks performed during MP and the tools used for evaluating upper extremity function differed, making it difficult to measure the actual changes. Furthermore, the authors of a previous study [12] claimed that patients with motor recovery after a stroke episode that occurred within the previous 6 months (subacute) did not benefit from MP. These patients had performed tasks such as opening, grasping, and lifting household objects; however, upper extremity function was measured by means of the ARAT, which led to differences between the tasks and the evaluation method. In order to evaluate the actual changes in a patient’s motions, we used objective three-dimensional (3D) motion analysis to investigate the identical motions that correspond to MP (in this case, drinking from a cup and opening a door).

The patients assessed in previous MP studies usually had chronic stroke, and few such studies have been performed in patients with subacute stroke. Because our hospital treats mainly those with subacute stroke, we focused on the effects of MP in this group.

In order to participate in therapy and follow instructions, patients undergoing traditional studies of MP and upper limb function [1, 8, 10, 11, 12, 13, 15, 16] are required to have good cognitive scores on the Mini-Mental State Examination (MMSE) or stable mental status, as well as the ability to understand verbal instructions. However, adequate MMSE scores and compliance with instructions alone are not sufficient to validate the effectiveness of MP. Therefore, our investigation cites studies on motor imagery [17,18], evaluating patients using a standard score of 2.26 on the Vividness of Movement Imagery Questionnaire (VMIQ).

In the present study, additional MP was provided to stroke patients who practiced conventional occupational therapy and performed identical tasks along with MP. Moreover, 3D motion analysis was carried out to understand the effects of MP on upper extremity function in real life after a stroke. We also compared the outcomes of 3D motion analysis and of clinical assessments (FMA-UE and MAL) to detect evidence of any congruity between these methods of evaluation.

Continue —-> KoreaMed Synapse

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[ARTICLE] Kinematic analysis of upper limb motion: Feasibility, preliminary results in controls and hemiparetic subjects, prospects


The aim of this study is to develop a valid and standardized instrumental analysis of upper limb (UL) motion in stroke patients.


Sixteen controls and 15 hemiparetic subjects (mean age = 54 ± 18,2 years old; Fugl-Meyer Upper Limb 41,4 ± 12,4) underwent kinematic motion analysis (passive markers, Optitrack) of pointing and grasping tasks. We examined the ability to perform a single pointing task and three reach-to-grasp tasks: key turning, reaching and grasping a can, reaching and grasping a cube; at a self-selected speed and as fast as possible. Speed, accuracy and efficiency of each movement were quantified and compared between controls and hemiparetic subjects, and between the ipsilateral of control subjects and the affected side; to describe reaching and grasping.


For reaching, movement time of hemiparetic UL was longer, less smooth (peak velocity, jerk), less direct (higher index path ratio) and associated with more trunk compensation (higher trunk/hand ratio). Movement time, jerks and trunk/hand ratio were the most discriminant variables between hemiparetic UL and ipsilateral/control UL, in any task analysed. Trunk displacement was greater in grasping than in reaching tasks. For grapsing tasks, movement time is the most discriminant factor between hemiparetic and control/ipsilateral UL, especially for the key turn task. Movement alterations were also found for ipsilateral limb. Association between kinematic variables and clinical features during reaching time (Fugl-Meyer, MAL, WFMT, ARAT) was greater for the task “grasping a can”.


Our results are similar to those of the literature, but suggest that we have to privilege some of the most relevant kinematic parameters. This standardization phase emerging after a validation phase of the techniques can make the biomechanical analysis of the upper limb as easy and valid as gait analysis and should help to develop the quantified measurement of prehension. This protocol is currently in process to objectively assess the therapeutic effects of rehabilitation treatments (botulinum toxin, induced constraint therapy).

Source: Kinematic analysis of upper limb motion: Feasibility, preliminary results in controls and hemiparetic subjects, prospects

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[REVIEW] Assessment of movement quality in robot- assisted upper limb rehabilitation after stroke – Full Text PDF

Studies of stroke patients undergoing robot-assisted rehabilitation have revealed various kinematic parameters describing movement quality of the upper limb. However, due to the different level of stroke impairment and different assessment criteria and interventions, the evaluation of the effectiveness of rehabilitation program is undermined.This paper presents a systematic review of kinematic assessments of movement quality of the upper limb and identifies the suitable parameters describing impairments in stroke patients. A total of 41 different clinical and pilot studies on different phases of stroke recovery utilizing kinematic parameters are evaluated. Kinematic parameters describing movement accuracy are mostly reported for chronic patients with statistically significant outcomes and correlate strongly with clinical assessments. Meanwhile, parameters describing feed-forward sensorimotor control are the most frequently reported in studies on sub-acute patients with significant outcomes albeit without correlation to any clinical assessments. However, lack of measures in coordinated movement and proximal component of upper limb enunciate the difficulties
to distinguish the exploitation of joint redundancies exhibited by stroke patients in completing the movement. A further study on overall measures of coordinated movement is recommended.

Full Text —> Assessment of movement quality in robot- assisted upper limb rehabilitation after stroke

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