Masaki Sekine

Classification of waist-acceleration signals in a continuous walking record

We attempted to distinguish walking on level ground from walking on a stairway using waist acceleration signals. A triaxial accelerometer was fixed to the subjects waist and the three acceleration signals were recorded by a portable data logger at a sampling rate of 256 Hz. Twenty healthy male subjects were asked to walk through a corridor and up and down a stairway as a single sequence, without any instruction. The data were analyzed using discrete wavelet transform. Walking patterns were classified in two stages. In the first stage, the times when the walking pattern changed were detected using the low-frequency component of the anteroposterior acceleration (LF(A)) and of the vertical acceleration (LF(V)). In the second stage, the three types of walking patterns were classified by comparing powers of wavelet coefficients in the vertical direction (P(WCV)) and in the anteroposterior direction (RP(WCA)). Changes in walking patterns could be detected by using either LF(A) or LF(V). Walking down stairs could be distinguished from the other types of walking as it gave the largest value in P(WCV), and walking up stairs could be discriminated from level walking using RP(WCA). Level and stairway walking could be classified from continuous records of waist acceleration.

Classification of walking pattern using acceleration waveform in elderly people

We attempted to classify walking on level ground from walking on a stairway using a waist acceleration signal. A tri-axial accelerometer was fixed to the subjects waist and the three acceleration signals were recorded by a portable data logger at a sampling rate of 256 Hz. Eleven healthy, elderly subjects were asked to walk through a corridor and up and down a stairway as a single sequence, without any instruction. The data were analyzed using a discrete wavelet transform. Walking patterns were classified using two parameters; one was the ratio between the power of wavelet coefficients which were corresponded to locomotion and total power in the anteroposterior direction (RPA). The other was the ratio between root mean square of wavelet coefficients at the anteroposterior direction and that at the vertical direction (RAV). Walking up stairs could be distinguished by the smallest value in RPA from other walking patterns. Walking down stairs could be discriminated from level walking using RAV. It was possible to classify the walking pattern using acceleration signals in elderly people.

Classification of acceleration waveform in a continuous walking record

We attempted to distinguish walking on level ground from walking on a stairway using a waist acceleration signal. A triaxial accelerometer was fixed to the waist and the three acceleration signals were recorded by a portable data logger at a sampling rate of 256 Hz. Twenty healthy male subjects were asked to walk through a corridor, or up and down a stairway continuously without any instruction. The data were analyzed using discrete wavelet transform. Walking patterns were classified according to two steps. At the first step, the times when walking pattern changed were detected using the low-frequency component of the anterio-posterior acceleration (LF/sub A/) and of the vertical acceleration (LF/sub V/). At the second step, three types of walking patterns were distinguished by comparing powers of wavelet coefficients in the vertical direction (P/sub WCV/) and in the anterio-posterior direction (P/sub WCA/). Changes in walking patterns could be detected by using both LF/sub A/ and LF/sub V/. Walking down stairs could be distinguished from the other types of walking by the largest value in P/sub WCV/ and walking up stairs could be discriminated from level walking by P/sub WCA/. Level and stairway walking could be distinguished by waist acceleration.

Assessment of gait parameter in hemiplegic patients by accelerometry

A portable microprocessor-based data acquisition system was used to measure the acceleration of joints during walking in ambulatory subjects and to evaluate temporal and spatial parameters of the gait. The system consists of two triaxial accelerometers and a microdata logger with removable memory. After examination, the recorded data are downloaded to a personal computer to analyze the gait cycle, power spectrum, and attractor of two sites. Experiments were performed on 28 hemiplegic patients of different Brunnstrom stages (Br. stages). The orthogonal accelerations at the head of the fibula were measured when walking at a normal speed. The gait cycle for a subject of a low Br. stage was significantly larger than that for a high Br. stage. The frequency distribution of gait for a low Br. stage subject was significantly smaller than that for a high Br. stage. The attractors of normal subjects and unaffected sites were simple figures with small variance, while those of affected sites were complex forms with large variance. The attractor changes simple form with higher Br. stage. Thus, the authors could estimate the rehabilitation effect from the attractor. This monitor may permit a quantitative evaluation of the recovery of paralyzed patients, both in hospital and at home.