Anticipatory Fall Detection Methods

• Anticipatory fall detection is a set of techniques that forecast imminent falls by analyzing pre-impact sensor data using sliding-window classifiers, activity recognition, or future pose forecasting.
• Approaches include binary sliding-window classification, threshold-based prior-fall activity recognition, and future-state inference via graph neural networks and LSTM architectures.
• These methods employ diverse sensor modalities—from wearable inertial sensors to video-derived skeletons—to provide early alerts, though real-world deployment challenges persist.

Searching arXiv for the specified papers and closely related anticipatory fall detection work. arxiv_search.query({"^^^^^^^^7search_query7 OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^^^^^", "^^^^^^^^7max_results7 ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7^^^^}) arxiv_search.query({"^^^^^^^^7search_query7 fall detection\" OR ^^^^^^^^7all:\7 activity identification\" OR ^^^^^^^^7all:\7 detection system\"", "^^^^^^^^7max_results7 ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7^^^^}) Anticipatory fall detection denotes the class of methods that attempt to identify an impending fall before bodily impact, rather than only recognizing a fall after collapse. In the literature represented here, anticipation is realized in three distinct ways: binary sliding-window classification that can alarm when the input window overlaps the immediate pre-impact interval; explicit recognition of activities occurring before a fall; and future-pose forecasting followed by gait-state classification into stable, transient, and fall (&&&^^^^7search_query7^^^^&&&, &&&^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^&&&, &&&^^^^7max_results7^^^^&&&). The topic therefore sits at the intersection of wearable inertial sensing, plantar-pressure sensing, threshold logic, time-series deep learning, pose estimation, graph-based spatiotemporal modeling, and human-state forecasting.

^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^. Conceptual scope and task formulations

Anticipatory fall detection is not a single problem formulation. In "Physics Sensor Based Deep Learning Fall Detection System" (&&&^^^^7search_query7^^^^&&&), the model is fundamentally a binary classifier, but it is applied in a sliding-window fashion to streaming data, which produces an early warning effect as soon as a window overlapping the immediate pre-impact interval is classified as “fall.” In "A fall alert system with prior-fall activity identification" (&&&^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^&&&), the emphasis is different: the system detects the fall with a threshold algorithm and separately identifies the prior-fall activity, motivated by the claim that prior-fall activities have a strong correlation with the intensity of the fall. In "Anticipatory Fall Detection in Humans with Hybrid Directed Graph Neural Networks and Long Short-Term Memory" (&&&^^^^7max_results7^^^^&&&), anticipation is formalized most explicitly as future-state inference, with three gait states—stable, transient, and fall—and a forecasting horizon up to half a second before impact.

System	Primary modality	Anticipation mechanism
TSFallDetect	Dual-foot pressure + IMU	Sliding-window binary classification
Prior-fall alert system	Chest-worn IMU	Prior-fall activity identification plus threshold fall detection
Hybrid DGNN–LSTM	Video-derived ^^^^7max_results7^^^^D skeletons	Future pose prediction plus ^^^^7search_query7^^^^-class gait classification

A common misconception is that anticipatory fall detection always requires an explicit “pre-fall” or “near-fall” class. The cited work shows otherwise. One line of work obtains anticipation from repeated inference over overlapping windows (&&&^^^^7search_query7^^^^&&&), whereas another introduces an intermediate transient state and forecasts future skeletal motion before classification (&&&^^^^7max_results7^^^^&&&). This suggests that anticipation can emerge either from temporal labeling and inference geometry or from an explicit predictive state model.

^^^^7max_results7^^^^. Sensing modalities, hardware, and data acquisition

The embedded-sensor line of work represented by TSFallDetect uses two identical sensor-packages mounted on the user’s left and right foot, each built around an STM^^^^7search_query7max_results7^^^^F^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7search_query7^^^^ MCU and carrying one film-pressure sensor array, one IMU-9^^^^7search_query7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^ module, and an ATK-BLE radio (&&&^^^^7search_query7^^^^&&&). All ^^^^7max_results7search_query7^^^^ channels from both feet—^^^^7max_results7^^^^ voltage, ^^^^7 OR all:\7^^^^ attitude, ^^^^7 OR all:\7^^^^ acceleration, and ^^^^7 OR all:\7^^^^ gyroscope—are sampled synchronously at approximately ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^8 Hz. The stream is cut into overlapping fixed-length windows of ^^^^7 OR all:\7all:\7^^^^ samples with stride ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^, so each input is a PRESERVED_PLACEHOLDER_^^^^7search_query7^^^^ matrix. Kalman filtering is applied independently to each time series for de-noising, with the standard one-dimensional state-space model

PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^

PRESERVED_PLACEHOLDER_^^^^7max_results7^^^^

where PRESERVED_PLACEHOLDER_^^^^7search_query7^^^^ and process noise PRESERVED_PLACEHOLDER_^^^^7all:\7^^^^ is ignored. No batch-normalization or global feature scaling is applied, to preserve the physical interpretability of raw readings.

The prior-fall activity system uses a wearable device based on WeMos D^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^ Mini V^^^^7max_results7^^^^ (ESP-8^^^^7max_results7 OR all:\7 OR all:\7^^^^EX), MPU^^^^7 OR all:\7search_query7 OR all:\7search_query7^^^^, and a ^^^^7search_query7^^^^.7 V/^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7max_results7search_query7search_query7^^^^ mAh Li-ion battery, with accelerometer and gyroscope sampling at PRESERVED_PLACEHOLDER_^^^^7 OR all:\7^^^^ Hz (&&&^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^&&&). Four attachment points were compared—right upper arm, left chest, left wrist, and left ankle—and chest was selected because it yielded the highest activity-classification accuracy with XGBoost. The firmware continuously buffers ^^^^7all:\7^^^^ s of data, and when a fall is detected it transmits the last ^^^^7all:\7^^^^ s of raw data, corresponding to PRESERVED_PLACEHOLDER_^^^^7 OR all:\7^^^^, via Wi-Fi to a server. The server extracts five overlapping ^^^^7max_results7^^^^ s windows, computes features, runs the XGBoost classifier to recognize the prior-fall activity, and applies majority vote over the five windows.

The vision-based anticipatory framework extracts ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7max_results7^^^^ ^^^^7max_results7^^^^D keypoints per frame using a YOLOv8-Pose backbone, including shoulders, hips, knees, ankles, wrists, and elbows (&&&^^^^7max_results7^^^^&&&). Missing keypoints are linearly interpolated from the two preceding frames, outliers are likewise replaced by interpolation, and a ^^^^7max_results7^^^^nd-order Butterworth low-pass filter with cutoff ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7^^^^ Hz removes high-frequency jitter. All keypoints are then expressed relative to the “upper-body center” defined as the midpoint between shoulders and hips, yielding ^^^^7max_results7all:\7^^^^ input values per frame and invariance to camera translation. Sliding windows are ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7 OR all:\7^^^^ frames long, corresponding to approximately ^^^^7search_query7^^^^.^^^^7 OR all:\7^^^^ s at ^^^^7search_query7search_query7^^^^ Hz.

These acquisition choices imply different operational emphases. Foot-mounted pressure and IMU streams privilege distal gait and plantar-contact information; chest-mounted IMU streams privilege global trunk dynamics; and video-derived skeletons privilege articulated posture and kinematic structure. That implication is inferential, but it is consistent with the sensor layouts used in the cited systems.

^^^^7search_query7^^^^. Detection logic and model architectures

TSFallDetect centers on FallSeqTCN, a purely convolutional Temporal Convolutional Network tailored for binary fall versus non-fall classification (&&&^^^^7search_query7^^^^&&&). The model uses dilated causal convolution blocks, each containing two ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^-D convolutions with kernel size $k$, exponentially increasing dilation $d=1,2,4,\dots$, zero-padding to preserve sequence length, and ReLU activations. For a single channel $x(t)$, the dilated convolution output is

PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7^^^^

followed by

PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^

Every three SDC Blocks are wrapped in a bottleneck residual unit with output PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7max_results7^^^^, optionally using a PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7^^^^ skip convolution when channel dimensions change. After the final residual block, global average pooling across time reduces the PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7all:\7^^^^ tensor to PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7 OR all:\7^^^^, followed by a fully connected layer and softmax

PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7 OR all:\7^^^^

for class PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)77^^^^. Dropout may be inserted between convolutional layers, for example PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^8.

The prior-fall alert system separates activity recognition from fall detection (&&&^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^&&&). For activity recognition, data are represented in both Cartesian and spherical coordinates, with

PRESERVED_PLACEHOLDER_^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^9

Time-domain features include mean, standard deviation, and pairwise Pearson correlation. Five classifiers were compared: Decision Tree, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, and XGBoost, with XGBoost selected because it gave the highest overall accuracy on chest + Cartesian. For fall detection proper, three existing threshold algorithms were tested, and the ^^^^7search_query7^^^^-phase threshold algorithm of Chaitep and Chawachat was selected. Let

PRESERVED_PLACEHOLDER_^^^^7max_results7search_query7^^^^

be the instantaneous G-force. Fall is detected if there exist PRESERVED_PLACEHOLDER_^^^^7max_results7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^ such that Phase ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^ satisfies PRESERVED_PLACEHOLDER_^^^^7max_results7max_results7^^^^, Phase ^^^^7max_results7^^^^ satisfies PRESERVED_PLACEHOLDER_^^^^7max_results7search_query7^^^^, and Phase ^^^^7search_query7^^^^ satisfies PRESERVED_PLACEHOLDER_^^^^7max_results7all:\7^^^^.

The hybrid DGNN–LSTM framework decouples motion prediction and gait classification (&&&^^^^7max_results7^^^^&&&). The Directed Graph Neural Network defines ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7max_results7^^^^ nodes for body joints and directed edges for the kinematic hierarchy, with three adjacency matrices PRESERVED_PLACEHOLDER_^^^^7max_results7 OR all:\7^^^^, each augmented during training by a learnable offset PRESERVED_PLACEHOLDER_^^^^7max_results7 OR all:\7^^^^. Layer-wise propagation is given by

PRESERVED_PLACEHOLDER_^^^^7max_results77^^^^

where PRESERVED_PLACEHOLDER_^^^^7max_results7^^^^8 is ReLU. A ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^D temporal convolution then aggregates dynamics across the ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7 OR all:\7^^^^-frame window. The LSTM motion predictor takes PRESERVED_PLACEHOLDER_^^^^7max_results7^^^^9 frames of relative keypoints, uses two stacked LSTM layers with hidden size PRESERVED_PLACEHOLDER_^^^^7search_query7search_query7^^^^, dropout PRESERVED_PLACEHOLDER_^^^^7search_query7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^, and one fully connected layer mapping PRESERVED_PLACEHOLDER_^^^^7search_query7max_results7^^^^, and predicts future frames up to ^^^^7 OR all:\7search_query7search_query7^^^^ ms ahead. At inference, the pipeline is serial: raw frames PRESERVED_PLACEHOLDER_^^^^7search_query7search_query7^^^^ keypoints PRESERVED_PLACEHOLDER_^^^^7search_query7all:\7^^^^ LSTM PRESERVED_PLACEHOLDER_^^^^7search_query7 OR all:\7^^^^ predicted skeleton window PRESERVED_PLACEHOLDER_^^^^7search_query7 OR all:\7^^^^ DGNN PRESERVED_PLACEHOLDER_^^^^7search_query77^^^^ class probabilities.

A central methodological distinction emerges here. TSFallDetect and the prior-fall alert system rely on streams from body-worn sensors, while the hybrid DGNN–LSTM system relies on skeletal abstractions extracted from video. More importantly, TSFallDetect uses a single discriminative model over sensor windows, the prior-fall system uses modular thresholding plus activity recognition, and the DGNN–LSTM approach uses modular prediction plus classification. This suggests that anticipatory performance can be pursued through temporal convolution, feature-engineered recognition, or explicit dynamics forecasting.

^^^^7all:\7^^^^. Labeling strategies, training protocols, and performance

In TSFallDetect, each ^^^^7 OR all:\7all:\7^^^^-sample window is labeled according to whether any sample in that window belongs to a “fall” interval, producing two classes only: PRESERVED_PLACEHOLDER_^^^^7search_query7^^^^8 if the window contains or ends in falling motion, and PRESERVED_PLACEHOLDER_^^^^7search_query7^^^^9 for pure normal activity (&&&^^^^7search_query7^^^^&&&). The loss is standard cross-entropy,

PRESERVED_PLACEHOLDER_^^^^7all:\7search_query7^^^^

The system re-evaluates every ^^^^7 OR all:\7 OR all:\7^^^^ ms because the stride is ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^ sample at approximately ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^8 Hz, and the paper states that in practice the model can therefore raise an alarm up to about ^^^^7search_query7^^^^ s before the final impact sample of a fall event is ingested. Training uses 7^^^^7search_query7^^^^% of windows for training and ^^^^7search_query7search_query7^^^^% for testing, with Adam and default PRESERVED_PLACEHOLDER_^^^^7all:\7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^, PRESERVED_PLACEHOLDER_^^^^7all:\7max_results7^^^^. On UMAFall, the compared approaches achieved: SVM, Acc 7^^^^7 OR all:\7^^^^%, Prec ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7search_query7^^^^%, Recall ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^%; LSTM, Acc 7^^^^7 OR all:\7^^^^%, Prec ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7search_query7^^^^%, Recall ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^%; Decision Tree, Acc 9^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^%, Prec 8^^^^7all:\7^^^^%, Recall 77%, PRESERVED_PLACEHOLDER_^^^^7all:\7search_query7^^^^ 8^^^^7search_query7^^^^%; SeqTCN, Acc 9^^^^7max_results7^^^^%, Prec 8^^^^7all:\7^^^^%, Recall 8^^^^7 OR all:\7^^^^%, PRESERVED_PLACEHOLDER_^^^^7all:\7all:\7^^^^ 8^^^^7 OR all:\7^^^^%. On the in-house foot dataset, the results were: SVM, Acc 8^^^^7search_query7^^^^%, Prec ^^^^7search_query7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^%, Recall 8^^^^7search_query7^^^^%, PRESERVED_PLACEHOLDER_^^^^7all:\7 OR all:\7^^^^ ^^^^7all:\7 OR all:\7^^^^%; LSTM, Acc 77%, Prec ^^^^7max_results7 OR all:\7^^^^%, Recall 8^^^^7search_query7^^^^%, PRESERVED_PLACEHOLDER_^^^^7all:\7 OR all:\7^^^^ ^^^^7search_query7^^^^8%; Decision Tree, Acc 9^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^%, Prec ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7^^^^%, Recall ^^^^7 OR all:\7search_query7^^^^%, PRESERVED_PLACEHOLDER_^^^^7all:\77^^^^ ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7 OR all:\7^^^^%; SeqTCN, Acc 98%, Prec ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7search_query7^^^^%, Recall 8^^^^7search_query7^^^^%, PRESERVED_PLACEHOLDER_^^^^7all:\7^^^^8 9^^^^7search_query7^^^^%.

In the prior-fall activity system, the activity-recognition study compared five models via randomized search plus ^^^^7 OR all:\7^^^^-fold CV with 7^^^^7search_query7^^^^% train and ^^^^7search_query7search_query7^^^^% validation, and XGBoost on chest + Cartesian reached 8^^^^7 OR all:\7^^^^.^^^^7search_query7^^^^% overall accuracy, compared with 79% for Decision Tree, 7^^^^7 OR all:\7^^^^% for k-NN, ^^^^7 OR all:\77^^^^% for Naive Bayes, and ^^^^7all:\7search_query7^^^^% for SVM (&&&^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^&&&). The system recorded eleven daily-living activities, ^^^^7 OR all:\7all:\7^^^^ falls in eight compass directions, and ^^^^7all:\7search_query7^^^^ “fall-to-knees first” events in five directions. For threshold fall detection, the ^^^^7search_query7^^^^-phase algorithm achieved fall detection accuracy 88.9^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^%, sensitivity 7^^^^7search_query7^^^^.^^^^7all:\7all:\7 and specificity 89.9^^^^7search_query7^^^^%; for knees-first falls, it achieved 9^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^.^^^^7max_results7 OR all:\7^^^^% accuracy, 77.^^^^7 OR all:\7search_query7^^^^% sensitivity, and 9^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^.8^^^^7search_query7^^^^% specificity. The ^^^^7max_results7^^^^-phase algorithm had higher sensitivity but lower specificity, whereas the ^^^^7search_query7^^^^-phase algorithm produced the highest accuracy. Prior-fall activity recognition reached an overall average of 8^^^^7 OR all:\7^^^^.^^^^7max_results7 OR all:\7^^^^%, with WALK_UP, WALK_DOWN, JUMPING_JACK, and RUN all reported at ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7search_query7^^^^% accuracy, while WALK and STAND were 7^^^^7search_query7^^^^%, and UP was ^^^^7 OR all:\7search_query7^^^^%.

In the DGNN–LSTM system, the LSTM is pre-trained on OUMVLP-Pose for normal walking and fine-tuned on URFD, while the DGNN is trained on URFD with ground-truth labels (&&&^^^^7max_results7^^^^&&&). Dataset splits are 8^^^^7search_query7^^^^% train and ^^^^7max_results7search_query7^^^^% test, and within train an 8^^^^7search_query7^^^^/^^^^7max_results7search_query7^^^^ train/validation split is used for early stopping. The losses are

PRESERVED_PLACEHOLDER_^^^^7all:\7^^^^9

and

PRESERVED_PLACEHOLDER_^^^^7 OR all:\7search_query7^^^^

Optimization uses Adam with initial learning rate PRESERVED_PLACEHOLDER_^^^^7 OR all:\7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^, decay by factor ^^^^7search_query7^^^^.^^^^7 OR all:\7^^^^ every ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7 OR all:\7^^^^ epochs, and max epochs ^^^^7search_query7search_query7search_query7^^^^. The movement anticipation error PRESERVED_PLACEHOLDER_^^^^7 OR all:\7max_results7^^^^ had mean values ^^^^7search_query7^^^^.^^^^7search_query7search_query7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^, ^^^^7search_query7^^^^.^^^^7search_query7search_query7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^, ^^^^7search_query7^^^^.^^^^7search_query7search_query7search_query7 ^^^^7search_query7^^^^.^^^^7search_query7max_results7 and ^^^^7search_query7^^^^.^^^^7search_query7search_query7all:\7^^^^ at ^^^^7search_query7^^^^.^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^ s, ^^^^7search_query7^^^^.^^^^7max_results7^^^^ s, ^^^^7search_query7^^^^.^^^^7search_query7^^^^ s, ^^^^7search_query7^^^^.^^^^7all:\7^^^^ s, and ^^^^7search_query7^^^^.^^^^7 OR all:\7^^^^ s, respectively. For fall anticipation accuracy, DGNN only achieved ^^^^7search_query7^^^^.9^^^^7 OR all:\7^^^^8 at ^^^^7search_query7^^^^ ms, ^^^^7search_query7^^^^.9^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7max_results7^^^^ at ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7search_query7search_query7^^^^ ms, ^^^^7search_query7^^^^.9^^^^7search_query7 at ^^^^7max_results7search_query7search_query7^^^^ ms, ^^^^7search_query7^^^^.8^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7all:\7^^^^ at ^^^^7search_query7search_query7search_query7^^^^ ms, ^^^^7search_query7^^^^.78^^^^7 OR all:\7^^^^ at ^^^^7all:\7search_query7search_query7^^^^ ms, and ^^^^7search_query7^^^^.7^^^^7 OR all:\7all:\7^^^^ at ^^^^7 OR all:\7search_query7search_query7^^^^ ms, whereas LSTM + DGNN achieved ^^^^7search_query7^^^^.9^^^^7 OR all:\7^^^^8, ^^^^7search_query7^^^^.9^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7max_results7^^^^, ^^^^7search_query7^^^^.88^^^^7 OR all:\7^^^^, ^^^^7search_query7^^^^.9^^^^7search_query7search_query7 ^^^^7search_query7^^^^.888, and ^^^^7search_query7^^^^.89^^^^7all:\7^^^^ over the same horizons.

Taken together, these results show that “anticipation” is not evaluated identically across studies. TSFallDetect measures binary classification performance under a windowing scheme that yields lead time (&&&^^^^7search_query7^^^^&&&), the prior-fall system measures both threshold fall detection and activity recognition (&&&^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^&&&), and the DGNN–LSTM system measures both movement anticipation error and future-horizon recognition accuracy (&&&^^^^7max_results7^^^^&&&). Direct cross-paper metric comparison is therefore limited.

^^^^7 OR all:\7^^^^. Transient states, prior-fall context, and interpretive issues

The notion of a transient state is explicit only in the DGNN–LSTM framework, where the classifier distinguishes stable, transient, and fall (&&&^^^^7max_results7^^^^&&&). Formally, for a given frame index PRESERVED_PLACEHOLDER_^^^^7 OR all:\7search_query7^^^^, the DGNN outputs PRESERVED_PLACEHOLDER_^^^^7 OR all:\7all:\7^^^^ while PRESERVED_PLACEHOLDER_^^^^7 OR all:\7 OR all:\7^^^^ remains below the fall threshold. The paper also uses PCA on penultimate-layer features PRESERVED_PLACEHOLDER_^^^^7 OR all:\7 OR all:\7^^^^ to visualize trajectories in a two-dimensional latent space, and a “pre-fall” alert is raised when the projected trajectory approaches the boundary separating the transient cluster from the fall cluster. The stated benefit is that not every transient leads to fall, so explicit tracking can reduce false positives and support graded alerts such as “high-risk” and “imminent” rather than binary decisions.

A different contextualization appears in the prior-fall activity system, which treats the activity preceding a fall as informative in its own right (&&&^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^&&&). The activity vocabulary includes level walking, stair ascent, stair descent, vertical jumps, jumping jacks, running, sitting down from standing, standing up from sitting, sit-ups, and the complex transitions labeled UP and DOWN. The system therefore frames anticipation partly as contextual recognition: if the alert includes the prior-fall activity, caretakers may better manage the situation. This is not equivalent to forecasting the future state, but it adds semantic information about the sequence leading into the fall.

TSFallDetect does not introduce a transient or prior-fall label, and this is important for interpreting its “anticipatory” character (&&&^^^^7search_query7^^^^&&&). The model anticipates falls by virtue of sliding-window inference over a binary label space, not by learning a multi-stage fall ontology. The paper explicitly notes that there is no explicit early-fall class or multi-stage labeling and suggests that a three-class scheme—‘pre-fall,’ ‘in-fall,’ ‘normal’—might yield earlier and more reliable alarms.

These differences clarify an often-blurred distinction. Anticipation may refer to early temporal detection, semantic contextualization of the period before a fall, or explicit modeling of the dynamical transition from stable gait to irreversible collapse. The three cited systems occupy these three positions respectively.

^^^^7 OR all:\7^^^^. Limitations, deployment constraints, and future directions

The studies converge on several limitations. TSFallDetect reports only ^^^^7max_results7 OR all:\7^^^^ in-house trials from ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^ volunteer, alongside the UMAFall public dataset, and notes that more diverse real-world data are needed to improve generalization (&&&^^^^7search_query7^^^^&&&). The paper also states that on-device latency, power consumption, and robustness to BLE packet loss are not yet fully quantified. In addition, no ROC curves or false-alarm rates are presented, and no cross-validation is reported.

The prior-fall activity system uses data from young to middle-aged adults aged ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^8–^^^^7all:\7^^^^9, and the fall and knees-first experiments were performed by ^^^^7all:\7^^^^ adult volunteers aged ^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^8–^^^^7search_query7^^^^8 (&&&^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^&&&). The study explicitly notes that elderly gait is typically slower, with lower peak accelerations, so thresholds PRESERVED_PLACEHOLDER_^^^^7 OR all:\77^^^^ need retuning; chest-to-floor distance is smaller; some falls from sitting or stooping may not trip the fall thresholds; and reduced dynamic range may increase false negatives. No confidence intervals or formal hypothesis tests were reported. The summary also lists possible extensions such as re-collecting elderly data, adding a second sensor for multi-modal fusion, incorporating frequency-domain features, and shifting from purely reactive thresholds to a short-term predictive model such as LSTM or temporal CNN that flags “near-fall” signatures ^^^^7search_query7^^^^.^^^^7 OR all:\7^^^^–^^^^7id:(Qu et al., 2024) OR id:(Ruenin et al., 2022) OR id:(Cho et al., 1 Sep 2025)7^^^^ s before impact.

The hybrid DGNN–LSTM system similarly notes that URFD has only ^^^^7search_query7search_query7^^^^ fall sequences, limiting variety (&&&^^^^7max_results7^^^^&&&). It further states that the LSTM predictor is relatively simple and that more advanced architectures such as C-LSTM or Graph-Transformer may reduce anticipation error. Transient analysis is described as qualitative because it relies on PCA visualization rather than a formal boundary metric, and real-time deployment is said to require latency-optimized implementations such as TensorRT or FPGA.

A broader methodological issue is ecological validity. All three lines of work rely on constrained datasets and staged falls rather than extensive real-world elderly fall corpora. This suggests that reported accuracies and lead times should be interpreted as properties of specific sensing setups, labeling schemes, and experimental populations, not as universal estimates of field performance. At the same time, the studies collectively indicate a clear research trajectory: from threshold-based post hoc detection, to enriched contextual recognition, to explicit anticipatory modeling of transient gait dynamics and future body configuration.