diff --git a/app/tracking/__init__.py b/app/tracking/__init__.py
index a0f797a..7fb7ed0 100644
--- a/app/tracking/__init__.py
+++ b/app/tracking/__init__.py
@@ -317,6 +317,15 @@ class OneEuroFilter(GenericVelocityFilter):
         self._x_filtered = keypoints  # Position filter state
         self._dx_filtered = None  # Initially no velocity estimate
 
+    @overload
+    def _smoothing_factor(self, cutoff: float, dt: float) -> float: ...
+
+    @overload
+    def _smoothing_factor(
+        self, cutoff: Float[Array, "J"], dt: float
+    ) -> Float[Array, "J"]: ...
+
+    @jaxtyped(typechecker=beartype)
     def _smoothing_factor(
         self, cutoff: Union[float, Float[Array, "J"]], dt: float
     ) -> Union[float, Float[Array, "J"]]:
@@ -324,6 +333,7 @@ class OneEuroFilter(GenericVelocityFilter):
         r = 2 * jnp.pi * cutoff * dt
         return r / (r + 1)
 
+    @jaxtyped(typechecker=beartype)
     def _exponential_smoothing(
         self,
         a: Union[float, Float[Array, "J"]],
@@ -367,12 +377,11 @@ class OneEuroFilter(GenericVelocityFilter):
         """
         dt = (timestamp - self._last_timestamp).total_seconds()
         if dt <= 0:
-            raise ValueError("invalid timestamp")
+            raise ValueError(
+                f"new timestamp is not greater than the last timestamp; expecting: {timestamp} > {self._last_timestamp}"
+            )
 
-        # Compute velocity from current input and filtered state
-        dx = (
-            (keypoints - self._x_filtered) / dt if dt > 0 else jnp.zeros_like(keypoints)
-        )
+        dx = (keypoints - self._x_filtered) / dt
 
         # Determine cutoff frequency based on movement speed
         cutoff = self._min_cutoff + self._beta * jnp.linalg.norm(
@@ -392,9 +401,7 @@ class OneEuroFilter(GenericVelocityFilter):
         )
 
         # Apply low-pass filter to position with adaptive cutoff
-        a_cutoff = self._smoothing_factor(
-            jnp.asarray(cutoff), dt
-        )  # Convert cutoff to scalar if needed
+        a_cutoff = self._smoothing_factor(jnp.asarray(cutoff), dt)
         self._x_filtered = self._exponential_smoothing(
             a_cutoff, keypoints, self._x_filtered
         )