Design and validation of an auditory biofeedback system for modification of running parameters

Lorenzoni, Valerio; Berghe, Pieter Van den; Maes, Pieter-Jan; Bie, Tijl De; Clercq, Dirk De; Leman, Marc

doi:10.1007/s12193-018-0283-1

Cited by 25 publications

(36 citation statements)

References 34 publications

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“…The total mass of the stripped backpack with the electronic components strapped to the inside shell was equal to 1.6 kg. The same backpack has been used in previous studies intertwining locomotion and music (e.g., [13,29,30]). Subjects wore their habitual running footwear to reflect the usual running habits and to increase the ecological validity of the study.…”

Section: Interventionmentioning

confidence: 99%

“…Biofeedback helps to develop the connection between the extrinsic feedback and the internal sensory cues associated with the desired motor performance during the first phase of motor retraining (i.e., the early adaptation phase) [10]. A patch Sensors 2020, 20, 1720 5 of 16 was designed in Max MSP software (v7, Cycling'74, San Francisco, CA, USA) to provide the auditory biofeedback [13]. The concurrent auditory feedback consisted of a music track that was continuously synchronized to the step frequency of a runner.…”

Section: Interventionmentioning

confidence: 99%

“…The biofeedback consisted of pink noise that was superimposed onto the music. Importantly, the noise's intensity was perceivable and depended on the magnitude of axial peak tibial acceleration [5,13]. The past five values of axial peak tibial acceleration were averaged through a 5-point moving average [9].…”

Section: Interventionmentioning

confidence: 99%

“…The chosen target was similar to previous gait retraining studies [6][7][8][9]. Six levels of noise loudness were empirically created for good discretization [13]: % noise, % baseline axial peak tibial acceleration: 100%, >113%; 80%, 96%-113%; 60%, 80%-95%; 40%, 65%-79%; 20%, 48%-64; 0%, <48%. The noise loudness was calculated as a percentage of the root mean square of the amplitude level of the music.…”

Section: Interventionmentioning

confidence: 99%

“…Given that the inter-subject response to a reduction in axial peak tibial acceleration can vary [7], one might expect an individual evolution in magnitude and presumably also in the timing of the change next to the evolution of the whole group. Although the technical aspect of augmented feedback systems is developing rapidly ( Table 1), little attention has been paid to when or how people interact with biofeedback on a running gait parameter [13,14], while this is imperative for understanding motor adaptations induced by the feedback parameter. For example, a first session of gait retraining may comprise a half-hour of running, whereas a major change in the desired performance may already be achieved after several minutes.…”

Section: Introductionmentioning

confidence: 99%

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Change-Point Detection of Peak Tibial Acceleration in Overground Running Retraining

Berghe

Gosseries

Gerlo

et al. 2020

Sensors

Self Cite

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A method is presented for detecting changes in the axial peak tibial acceleration while adapting to self-discovered lower-impact running. Ten runners with high peak tibial acceleration were equipped with a wearable auditory biofeedback system. They ran on an athletic track without and with real-time auditory biofeedback at the instructed speed of 3.2 m·s −1 . Because inter-subject variation may underline the importance of individualized retraining, a change-point analysis was used for each subject. The tuned change-point application detected major and subtle changes in the time series. No changes were found in the no-biofeedback condition. In the biofeedback condition, a first change in the axial peak tibial acceleration occurred on average after 309 running gait cycles (3 40"). The major change was a mean reduction of 2.45 g which occurred after 699 running gait cycles (8 04") in this group. The time needed to achieve the major reduction varied considerably between subjects. Because of the individualized approach to gait retraining and its relatively quick response due to a strong sensorimotor coupling, we want to highlight the potential of a stand-alone biofeedback system that provides real-time, continuous, and auditory feedback in response to the axial peak tibial acceleration for lower-impact running.

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Section: Interventionmentioning

confidence: 99%

Section: Interventionmentioning

confidence: 99%

Section: Interventionmentioning

confidence: 99%

Section: Interventionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Change-Point Detection of Peak Tibial Acceleration in Overground Running Retraining

Berghe

Gosseries

Gerlo

et al. 2020

Sensors

Self Cite

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Sound Design and the Theory of Self-augmented Interactions

Leman

2024

Current Research in Systematic Musicology

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In the past decades, musicology has been evolving at a pace that matches new developments in technology. Underneath this development, a new theory of music emerged, embracing interaction states as a model for understanding how music can be empowering. In the present chapter, sound design is considered from the viewpoint of interaction states, using caregiver–infant communication as a challenging domain of application. Sound design components of interest are identified, as well as human capacities for dealing with them in terms of empowerment. These are related to the concepts of self-augmented interaction and biofeedback-based sound design.

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